User interest

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Personalized Ranking Algorithm Based on User Interest Modeling

Personalized Ranking Algorithm Based on User Interest Modeling

Abstract. It’s convenient for Internet users to access to web resources with a search engine. However, most traditional search engines can’t provide personalized search results for users. In order to overcome this limit, we adopt a combination method of the explicit and implicit user modeling to build and update a user interest model. To be specific, we first build the user interest model with a topic-based representation method by the information offered by users. Then we update the model by considering the time factor and the user browsing behavior. As nouns are obviously more distinctive than other words, we give a greater weight for them. Based on this, we have improved the BM25 algorithm. Finally, a personalized ranking algorithm combining topic ranking and BM25 ranking is proposed. The experiments show that the personalized ranking algorithm based on user interest modeling can provide personalized search service for users.
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A NOVEL APPROACH TO PAGE RANKING MECHANISM BASED ON USER INTEREST

A NOVEL APPROACH TO PAGE RANKING MECHANISM BASED ON USER INTEREST

Many algorithm in the area of personalization have been developed in past which are based on the user interest and user browsing history. In [1] Surgey Brin and Larry Page give the ranking algorithm named as Page Rank (PR) which used by the search engine Google. Google used the Page rank algorithm to rank the web pages. Page rank algorithm is classified in the web structure mining technique where algorithm is based on link structure of web pages. Page rank algorithm tells that both incoming and outgoing link is important and in this algorithm page rank is calculated by added all backlinks rank and final page rank score is calculated. In [6] Page Ranking based on Link Visit (PRLV) user browsing information is additional used to the original Google Page rank algorithm. PRLV is based on the web structure and web usage mining. Here user search behaviour is also considered. In this algorithm outgoing links which is more visited having the more rank score then less visited pages. User profiles can be created explicitly and implicitly. In [7] personalized search system is based on the web usage mining and user profile is created explicitly. Here user profile is created using the user browsing history and it attributes such as no of page visited, no of page clicked, time spent on web page and action performed on page. Previous history is used to re–rank the search results and using these factor more users relevant results are obtained. In [5] proposed the personalized search engine model that is based on the web usage mining. Here user profile is created when user registers first time on the system. User is asked to enter their interest area explicitly which keeps on adding their interest area depending on user browsing patterns. Feature words are extracted from the web page visited by the users which are further used to create the short term and long term user profile.
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A Microblog User Interest Model Based on Participated and Not Participated Data

A Microblog User Interest Model Based on Participated and Not Participated Data

Aiming at the problem of predicting the real-time interest of user, this paper proposes a user interest model based on the participated data and the not participated data. Experiments show that user interest models in proposed framework perform better than those in traditional framework. Existing methods mainly rely on the participated data but the results of experiments find that the not participated data plays a more important role. In further research work, we will concentrate on optimizing the size of time window dynamically and recommending useful information to users with their real-time interests.
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Product Recommendation System Based on User Interest, Location and Social Circle

Product Recommendation System Based on User Interest, Location and Social Circle

Recommendation System (RS) is utilized to discover users interested things. With the start of social system, individuals are interested to share their experience, for instance, rating, audits, and so forth that has any kind of effect to prescribe the things of user interest. Few recommendation frameworks has proposed that depend on collaborative filtering, content based filtering and hybrid recommendation methodologies. The present recommendation system is not productive as want. It needs to require improvement in structure for present and future necessities to getting best outcomes for recommendation characteristics. This paper utilizes four factors, for example, social components, personal interest comparability, interpersonal effect and user's location data. Blend of these components is used into a brought together personalized recommendation show which is relies upon probabilistic network factorization. In propose system we include user location in dataset additionally utilize PCC similitude technique which diminish blunders and affiliation rules mining utilizing FP-Growth which enhances the accuracy.
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Symbolize Recommendation Linking User Interest and Social Circle

Symbolize Recommendation Linking User Interest and Social Circle

Recommender system (RS) has been successfully exploited to solve information overload. In ECommerce, like Amazon, it is important to handling mass scale of information, such as recommending user preferred items and products. A survey shows that at least 20 percent of the sales in Amazon come from the work of the RS. It can be viewed as the first generation of Rses with traditional collaborative filtering algorithms to predict user interest. However, with the rapidly increasing number of registered users and various products, the problem of cold start for users (new users into the RS with little historical behavior) and the sparsity of datasets (the proportion of rated user- item pairs in all the user-item pairs of RS) have been increasingly intractable.
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Collaborative Filtering Algorithm over Ecommerce Website Based on User Interest

Collaborative Filtering Algorithm over Ecommerce Website Based on User Interest

ABSTRACT: Collaborative filtering algorithm is one of widely used approaches in daily life, so how to improve the quality and efficiency of collaborative filtering algorithm is an essential problem. Usually, some traditional algorithm focuses on the user rating, while they don't take the user rating differences and user interest into account. However, users who have little rating difference or have a similar interest may be highly similar. In this paper, a collaborative filtering algorithm based on scoring difference and user interest is proposed. Firstly, a rating difference factor is added to the traditional collaborative filtering algorithm, where the most appropriate factor can be obtained by experiments. Secondly, calculate the user's interest by combining the attributes of the items, then further calculate the similarity of personal interest between users. Finally, the user rating differences and interest similarity are weighted to get final item recommendation and score forecast. The experimental results on data set show that the proposed algorithm decreases both Mean Absolute Error and Root Mean Squared Error, and improves the accuracy of the proposed algorithm.
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Analysis and control of information diffusion dictated by user interest in generalized networks

Analysis and control of information diffusion dictated by user interest in generalized networks

The diffusion of useful information in generalized networks, such as those consisting of wireless physical substrates and social network overlays is very important for theoreti- cal and practical applications. Contrary to previous works, we focus on the impact of user interest and its features (e.g., interest periodicity) on the dynamics and control of diffusion of useful information within such complex wireless-social systems. By con- sidering the impact of temporal and topical variations of users interests, e.g., seasonal periodicity of interest in summer vacation advertisements which spread more effec- tively during Spring–Summer months, we develop an epidemic-based mathematical framework for modeling and analyzing such information dissemination processes and use three indicative operational scenarios to demonstrate the solutions and results that can be obtained by the corresponding differential equation-based formalism. We then develop an optimal control framework subject to the above information diffu- sion modeling that allows controlling the trade-off between information propagation efficiency and the associated cost, by considering and leveraging on the impact that user interests have on the diffusion processes. By analysis and extensive simulations, significant outcomes are obtained on the impact of each network layer and the associ- ated interest parameters on the dynamics of useful information diffusion. Furthermore, several behavioral properties of the optimal control of the useful information diffusion with respect to the number of infected/informed nodes and the evolving user inter- est are shown through analysis and verified via simulations. Specifically, a key finding is that low interest-related diffusion can be aided by utilizing proper optimal controls. Our work in this paper paves the way towards this user-centered information diffusion framework.
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Movie Recommendation with User Interest in Social Network

Movie Recommendation with User Interest in Social Network

Recommendation system (RS) has been successfully used to solve problem overwhelming. Social networks such as facebook, twitter are handling large scale of information by recommending user interested items and products. RS has wide range of applications such as research articles, new social tags, movies, music etc. According to the user input and different attribute items can be recommended, which is closely related to user interest.

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Efficient Web Search Based On User Interest and Browsing History

Efficient Web Search Based On User Interest and Browsing History

In this paper the location and content concept has been separated and is organized into different ontology to make an ontology-based, multi-facet (OMF) profile which is captured by web history and location interest. This model actually gives results by outlining the concepts in accordance with the preference of user. By keeping the diverse interest of the users in mind, location entropy is introduced for finding the degree of interest and information related to location and query. The personalized entropies actually establish the relevant output content and location content. At last, an SVM based on the ontology is derived which can be used for future purpose for ranking or re-ranking. The experiments show that the results produced by OMF profiles are more accurate in comparison with the ones which use baseline method [5]. Proposed a personalized web search model that combines community based and content based evidences based on novel ranking technique. Nowadays, uploading data on internet has become a daily activity. A massive amount of data is uploaded in the form of web pages, news, and blogs etc. on a regular basis. So, it becomes very difficult for the user to search for relevant content. Not only for users but also for search engines like Google and Yahoo it becomes difficult. Information overload is the only reason behind this difficult situation. Other than this user's preference is the second problem, which is not taken into consideration while producing the results. The author tried to solve this problem through this model which produces results on the basis of preference and interest of the user. In this paper, authors proposed a unique approach to find out the interest and preference of the user. It's a two way approach, first it will find out the activities of user through his/her profile in social networking sites. Secondly, it will find out information from what the social networking sites provide to the user through friends and community. Based on the results, user's interest and preference will be prioritized by the web search or it is personalized [6].
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Web Search and Recommendation-based on User Interest for PWS

Web Search and Recommendation-based on User Interest for PWS

Though various information retrieval methods (for instance, web search engines applications and digital library systems) have been effectively installed, the present retrieval systems are far from optimal. A key deficiency of present retrieval schemes is that they usually lack of user modeling and are not adaptive to individual users. This characteristic non-optimality is seen openly in the subsequent two cases: (1) Different users can use the identical query (e.g., “Java”) to search for dissimilar information (for example, the Java island located in Indonesia or the Java programming language), however existing IR methods return the identical results for these users. Without considering the actual user, it is difficult to know which sense “Java” refers to in a query. (2) A user’s data needs can change over time. The similar user can use “Java” sometimes to mean the Java Island in Indonesia and some other times to mean the programming language. It would be impossible to recognize the correct sense without recognizing the search context.
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ADVERTISEMENT RECOMMENDATION SYSTEM BASED ON USER INTEREST

ADVERTISEMENT RECOMMENDATION SYSTEM BASED ON USER INTEREST

Abstract: The report describes and evaluates privacy-friendly methods to reach to the correct customer by posting the advertisement on social networking sites. The purpose of the report is finding good audience for brand advertising. Targeting social-network neighbors resounds well with advertisers, and on-line browsing behavior data counter unthinkingly can allow the identification of good audiences anonymously. This report introduces a framework for evaluating brand audiences with the help of Ads Recommendation. This introduces methods of extracting the specified users of social networks from data on visitations to social networking pages, without collecting any information on the identities of the browsers or the content of the social-network pages. The report introduces measure of Online Recommendation System. Fine-Grained user are sorted are provided to the advertiser by using the Ads Recommendation Algorithm. To implement the Ads Recommendation system, a Social Networking Platform is created. To implement the proposed algorithm Pattern Matching and Machine Learning concepts are used to implement the project. As by using the ads recommendation system the sorted users are provided to the advertiser as it will maximize the actual conversion ratio of user to the customer. Finally, the evidences are provided that the quasi-social network embeds a true social network along with results from social theory offers ones explanation for the increase in audience brand affinity.
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Personalized Movie Recommendation System with...

Personalized Movie Recommendation System with...

For example, it is observed that 90% of people have trust on book recommended by friends is good, and 75% of people have trust that the recommendation is useful from friends. People who like night life are likely to buy things related night life. These kinds of factors motivated to develop a recommendation system which includes factors like user interest, social influence, inter personal influence. The purpose of Recommendation Framework is to actually make things to be proposed automatically (Movie, Music, Books, etc.) according to their historical behavior and reduce their seeking time on the web.
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MODELING AND SIMULATION OF GRID CONNECTED PHOTOVOLTAIC DISTRIBUTED GENERATION 
SYSTEM

MODELING AND SIMULATION OF GRID CONNECTED PHOTOVOLTAIC DISTRIBUTED GENERATION SYSTEM

For the three actions, save pages, add pages to Favorites and print pages, there is no issue on the magnitude of the three actions. They can only have two states, occurring or not occurring. It indicates a relatively high degree of the user interest when the three actions occur. There is no need to further analyze the times of a certain page visiting and the dwell time on a page in such situation. Therefore, the function is given the greatest interest degree 1 when the three actions occur, and stop to continue analyzing the other two behavior functions.

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Survey on Different Page Ranking Algorithms Based on Links and Time

Survey on Different Page Ranking Algorithms Based on Links and Time

ABSTRACT: Page Rank is extensively used for ranking web pages. There are many algorithms for page ranking such as Page Rank algorithm, Weighted Page Rank algorithm, Enhanced- ratio rank algorithm, Page Ranking Based on the Reading Time etc. The page ranking algorithm reflects the popularity of a web page in its page rank score. Due to the changing nature of web number of web pages are deleted and added newly. Every time a surfer searches web using the search engine, data should be fresh and relevant. So, to retrieving efficient, relevant and meaningful information from large sources of information is very challenging job and also it is very difficult to retrieving information which based on the user interest.The main aim of this paper is to discuss the various existing page ranking algorithms and the modification done to the standard page rank algorithm.
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Travel Recommendation Approach using Collaboration Filter in Social Networking

Travel Recommendation Approach using Collaboration Filter in Social Networking

In this Paper , here propose a user-service rating prediction approach by exploring users’ rating behaviors with considering four social network factors: user personal interest (related to user and the item’s topics), interpersonal interest similarity (related to user interest), interpersonal rating behavior similarity (related to users’ rating habits), and interpersonal rating behavior diffusion (related to users’ behavior diffusions). A concept of the rating schedule is proposed to represent user daily rating behavior. The similarity between user rating schedules is utilized to represent interpersonal rating behavior similarity. The factor of interpersonal rating behavior diffusion is proposed to deep understand users’ rating behaviors. We explore the user’s social circle, and split the social network into three components, direct friends, mutual friends, and the indirect friends, to deep understand social users’ rating behavior diffusions. These factors are fused together to improve the accuracy and applicability of predictions. We conduct a series of experiments in Yelp and Douban Movie datasets. The experimental results of our model show significant improvement. To further enhance the recommendation, check-in behaviors of users will be deeply explored by considering the factor of their multi-activity centers and the attribute of POIs.
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Product Purchase Recommendation Of User By Data Analysis Using Data Mining

Product Purchase Recommendation Of User By Data Analysis Using Data Mining

In this paper, we propose a user-service rating prediction model based on probabilistic matrix factorization by exploring rating behaviors. Usually, users are likely to participate in services in which they are interested and enjoy sharing experiences with their friends by description and rating. Like the saying “birds of a feather flock together,” social users with similar interests tend to have similar behaviors. It is the basis for the collaborative filtering based recommendation model. Social users’ rating behaviors could be mined from the following four factors: personal interest, interpersonal interest similarity, interpersonal rating behavior similarity, and interpersonal rating behavior diffusion. Why do we consider these four factors? In our opinion, the rating behavior in recommender system could be embodied in these aspects: when user rated the item, what the rating is, what the item is, what the user interest we could dig from his/her rating records is, and how user’s rating behavior diffuse among his/her social friends. In this paper, we propose a user-service rating prediction approach by exploring social users’ rating behaviors in a unified matrix factorization framework.
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Designing an adaptive online advertisement system : a focus group methodology

Designing an adaptive online advertisement system : a focus group methodology

More concretely, our initial system design for the first version of MyAds used an exploratory design methodology, where participants were asked to produce a list of system requirements and answer a questionnaire regarding their perceptions about what can a future personalised e-advertising system provide [6]. The data collected from the exploratory study was used in the first system implementation iteration [1]. The main features introduced as a result of the exploratory experiment and were implemented in the first iteration of MyAds included: user profiling via matching user interest and gender with products, social capability to interact, chat, comments about the advertisement, multiple advertisements based on the stated interests, a proportional recommendation of advertisements with the user interests based on the weights of each interest. The higher the weight of the interest users will get more related items to this interest. However, some of the main drawbacks from the previous experiment included issues with the design of the system and the need of richer user profiling and recommendations, so the need for an updated design has emerged. The methodology used for the second system design phase was the focus group approach. The reason for using this approach is because there was a need for a more concrete interaction with the participants, with a larger amount and range of answers, and more focused responses.
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Exploring rating of product using collaborative filtering approach

Exploring rating of product using collaborative filtering approach

Abstract - In this work, we tend to propose a user- service rating prediction model supported probabilistic matrix factorization by exploring rating behaviors. Usually, users are seemingly to participate in services within which they are interested and revel in sharing experiences with their friends by description and ratings. Social users with similar interests tend to possess similar behaviors. It's the idea for the cooperative filtering primarily based recommendation model. Social users’ rating behaviors may well be well-mined from the subsequent four factors: personal interest, social interest similarity, social rating behavior similarity, and social rating behavior diffusion. By considering these four factors, the rating behavior in recommender system may well be embodied in these aspects: once user rated the item, what the rating is, what the item is, what the user interest we tend to might dig from his/her rating records is, and the way user’s rating behavior diffuse among his/her social friends. During this paper, we tend to propose a user- service rating prediction approach by exploring social users’ rating behaviors in a very unified matrix factorisation framework. we tend to found that users high on Openness tend to rate a lot of things than needed, whereas low Conscientiousness could be a essential issue that provokes users to rate things in an explosive method. Our findings are helpful for researchers curious about user modeling, preference stimulant, recommender systems and on-line promoting.
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PERSONALISED NEWS RECOMMENDATION SYSTEM BASED ON USER INTERESTS

PERSONALISED NEWS RECOMMENDATION SYSTEM BASED ON USER INTERESTS

It is clear that, having a central access point makes it significantly simpler to discover and access new content from a large number of diverse RSS sources. The initial step involved in this is to collect set of domain names or the website links required. Then, these domains are crawled for the valid RSS link. To get the RSS links from the domain we have to crawl the home page of the RSS links. This can be accomplished by extracting all the anchor tag and checking whether it is in valid RSS, ATOM, XML, RDF format. All advertisement-links are filtered appropriately by checking against the domain name. Certain news portal sites provide RSS service through third parties like feeds.feedburner.com so such links are not removed since they donot match domain name. Now these RSS links are stored in the database for future use in order to reduce crawling time and data usage. The RSS feed contents are mined for the user interest. Using the summary of the news articles, user interests are categorized accordingly using tf-idf (term frequency-inverse document frequency) model and the corresponding category is updated in the database. These RSS links are periodically checked in order to get the instant feeds. The news articles under each interest are processed to obtain the topics and recommended to those users. The topic modelling using Named Entity Recognition Technique is handled here to extract the crisp topics efficiently. Now, the crawler will fetch RSS links from database and extract required details like title, description, published date, article link etc. and store it in the database. Next step involved in this process is categorising news article based on the content. A well trained categorizer will categorise the news into certain predefined categories and store it correspondingly. It contains some additional features like finding similar articles and providing recommendation to the user. The detailed architecture of the system is given in fig. 2.
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Ontology Generation from Session Data for Web Personalization

Ontology Generation from Session Data for Web Personalization

-------------------------------------------------------------------ABSTRACT----------------------------------------------------------------- With an increasing continuous growth of information in WWW it is very difficult for the users to access the interested web pages from the website. Because day by day the information in the web is growing in an increasing manner so without any help system the user may spend more time to get the interested information from the website. To overcome the above problem, in this paper we propose a Model which create a User Interested Page Ontology (UIPO), it will be created by assigning weights and ranking the user interest by count the number of occurrence of each item which was collected from the web logs within a session for all users. The main feature of this model is, it generates UIPO dynamically from that it personalize the interested pages to the web users in their next access The proposed model is very useful for understanding the behavior of the users and also improving the web site design too. The performance of the new model in a session is also discussed in this paper.
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