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Recommendation in E Commerce using Collaborative Filtering

Recommendation in E Commerce using Collaborative Filtering

Recommenders system are the desire to improve users satisfaction and to increase economic success of the platform. In Ecommerce a recommender may either recommend a top recommendation based on the best price performance ratio for the customer but it may also show the products that are likely to lead to the highest revenue for the business.

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A Web Based Recommendation System for Personal Learning Environments Using Hybrid Collaborative Filtering Approach

A Web Based Recommendation System for Personal Learning Environments Using Hybrid Collaborative Filtering Approach

ABSTRACT: The large growth of Web has influenced the generation of huge e-learning resources. This work is focused to devise a personal recommendation system that will address the sparsity and cold-start problems and that will provide a have a more diverse recommendation list for each learner. Here Improved Neighborhood- based Collaborative filtering and Hybrid Genetic algorithm with Particle Swarm Optimization (PSO) method is implemented. These techniques are employed for improving the diversity, and the convergence towards the preferred solution taking into account the preferences of users. The results obtained from the experiments show that the proposed method outperforms current algorithms in terms of accuracy measures and can alleviate cold-start and sparsity problems and generate a more diverse recommendation list as well.
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Query Recommendation by using Collaborative Filtering Approach

Query Recommendation by using Collaborative Filtering Approach

sections of data about a query are typically manageable in list formats and regularly used numerous times between top-k retrieved documents. Therefore frequent lists combining inside the top-k search results are planned to mine query facets as well as implement a method called as QDMiner. More exactly, QDMiner saves lists from HTML tags, and free text surrounded in the top-k search results, combines them into groups depends on the items they surround, then orders the clusters as well as items based on in what manner the lists and items appear in the top-k results. The scheme includes two representations, one is the Unique Website Model, and another is the Context Similarity Model, to order or rank query facets. Moreover, to recommend user interested result, a collaborative filtering technique is used. As for a collaborative recommendation, there are two ways to estimate the correspondence for group recommendation: item-based and user-based.
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A Review on Personalized Information Recommendation System Using Collaborative Filtering

A Review on Personalized Information Recommendation System Using Collaborative Filtering

B. Sparsity and cold-start problem in Collaborative Filtering The numbers of users and items in major e-commerce recommendation systems is very large. Even users that are very active result in rating just a few of the total number of items available in a database and respectively, even very popular item result in having been rated by only a few of the total number of users available in the database. This problem is called sparsity problem. This has a major negative impact on the effectiveness of a collaborative filtering approach. Because of sparsity, it is possible that the similarity between two users cannot be defined, rendering collaborative filtering useless. Even when the evaluation of similarity is possible, it may not be very reliable, because of insufficient information processed. The cold-start problem emphasizes the importance of sparsity problem. Cold-start refers to the situation in which an item cannot be recommended unless it has been rated by a substantial number of users. This problem applies to new and obscure items and is particularly detrimental to users with eclectic taste. Likewise, a new user has to rate a sufficient number of items before the recommendation algorithm be able to provide reliable and accurate recommendations [6].
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Book Recommendation System using Item Based Collaborative Filtering

Book Recommendation System using Item Based Collaborative Filtering

Over a last few years recommendation systems are used widely in almost every business in the market. Best examples of recommendation systems are given by Amazon, Ebay etc. This paper discusses various methods that can be used to build a recommender system but implemented an item-based collaborative filtering approach on “goodbooks10k” dataset found on kaggle. Also the implementation of the experiment and the results are presented in the paper.

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Scalable Filtering Approaches for Recommendation Systems in E-Commerce

Scalable Filtering Approaches for Recommendation Systems in E-Commerce

that those individuals agreed in the past tend to agree again in the future. Another filtering technique is Content-Based filtering which makes recommendations based on the users previous choices or interests. Personalized profiles are created automatically through user feedback, and they describe the type of items a person likes. In order to achieve better recommendation results the collaborative filtering and content based filtering techniques can be combined to build hybrid recommender systems. Knowledge-based systems recommend items based on specific domain knowledge about how certain item features meet users needs and preferences and, ultimately, how the item is useful for the user. In these systems a similarity function estimates how much the user needs (problem description) match the recommendations (solutions of the problem). The similarity score or the calculated prediction can be directly interpreted as the utility of the recommendation for the user.
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Web Service Recommendation using Collaborative Filtering

Web Service Recommendation using Collaborative Filtering

In this study, Pearson Correlation Coefficient and Normal Recovery Collaborative Filtering methodology is developed for web service recommendation. Proposed NRCF is combination of NRCF similarity measures and PCC clustering and prediction. So that proposed NRCF approach investigates the characteristics of web service QoS values and propose a new similarity measure, which finds similar users (or web services) more accurately and leads to better QoS value prediction accuracy. By systematically fusing the information of similar users and similar web services, proposed NRCF approach can achieve better prediction accuracy. Experiments are conducted on real-world web service QoS data set. The experimental results show that our method significantly improves the QoS value prediction accuracy compared with PCC approach.
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Optimized travel recommendation using location based collaborative filtering

Optimized travel recommendation using location based collaborative filtering

recommendation.A customized travel arrangement suggestion is proposed when a client is going to visit another place. Web-based i.e. social networking - based proposal methodologies are powerful and effective, however experiences the notable "time multifaceted nature issue and cost fulfillment" in suggestion frameworks, because of travel information being exceptionally scanty. In this situation, it makes exact comparative client recognizable proof exceptionally troublesome if the client has just gone by a little number of POIs.The classification points are typically dictated by the guileless class data from suggested frameworks in Topic Model Method(TM). From the foreordained classifications, it is helpful to ascertain client inclinations. Shockingly, for rich photograph sharing systems like Flickr, there is no such characterized class data. Along these the topic based suggestion approach can't be used specifically in travel proposals, accordingly by utilizing location based collaborative filtering method, the group contributed photographs are utilized to give customized venture out succession arrangements to enthusiastic explorers.
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An Improved Collaborative Filtering Recommendation Algorithm

An Improved Collaborative Filtering Recommendation Algorithm

based method and model based method [3]. The memory based method calculates the similarity of different users and forms a similar neighbor set by the order of their similarity. Recommendations are made by using the ratings of the similar neighbors in a user’s neighbor set. The model based method builds up a model to describe the network behaviors of a user first and then predicts the ratings of this user according his behavior model. Compared with the model based method, the memory based method can achieve higher accuracy of recommendations. But as the number of users and items grows, the cost time of similarity calculation increases rapidly and it may fails to respond in real time. The model based method performs better in speed because most of the work is the off-line model-building work. Besides of collaborative filtering, there are other algorithms like content-based method technique[4], social recommendation[5] and semantic recommendation[6].
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Data Preprocessing for Evaluation of Recommendation Models in E-Commerce

Data Preprocessing for Evaluation of Recommendation Models in E-Commerce

Some e-commerce sites could be a source for retailers to use purchased products for resale or development of other goods. These individuals are referred to as B2B customers, and it has been observed that their characteristics differ from the common visitors in the following aspects: (i) they purchase twice as much as B2C customers [13] and several sites supporting B2B e-commerce provide bulk buying options and bulk pricing [27]; (ii) they purchase rationally compared to possible impulsive buys from B2C customers [28]. In such cases, it becomes very important to identify such buyers specifically, as they are not the target audience for our recommendation model and greatly inflate the purchase data. Furthermore, B2B customers often have a relationship specified with e-commerce businesses on the specific products they need, and these individuals mostly purchase from that standard set of products [29].
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RECOMMENDATION ALGORITHM: ITEM-BASED COLLABORATIVE FILTERING

RECOMMENDATION ALGORITHM: ITEM-BASED COLLABORATIVE FILTERING

Model-based collaborative filtering algorithms provide item recommendation by first developing a model of user ratings. Algorithms in this category take a probabilistic approach and envision the collaborative filtering process as computing the expected value of a user prediction, given his/her ratings on other items. The model building process is performed by different machine learning algorithms such as Bayesian network, clustering, and rule-based approaches. The Bayesian network model formulates a probabilistic model for collaborative filtering problem. Clustering model treats collaborative filtering as a classification problem and works by clustering similar users in same class and estimating the probability that a particular user is in a particular class C, and from there computes the conditional probability of ratings. The rule-based approach applies association rule discovery algorithms to find association between co-purchased items and then generates item recommendation based on the strength of the association between items
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Survey on Collaborative Filtering, Content based Filtering and Hybrid Recommendation System

Survey on Collaborative Filtering, Content based Filtering and Hybrid Recommendation System

Recommender systems or recommendation systems are a subset of information filtering system that used to anticipate the 'evaluation' or 'preference' that user would feed to an item. In recent years E-commerce applications are widely using Recommender system. Generally the most popular E- commerce sites are probably music, news, books, research articles, and products. Recommender systems are also available for business experts, jokes, restaurants, financial services, life insurance and twitter followers. Recommender systems have formulated in parallel with the web. Initially Recommender systems were based on demographic, content-based filtering and collaborative filtering. Currently, these systems are incorporating social information for enhancing a quality of recommendation process. For betterment of recommendation process in the future, Recommender systems will use personal, implicit and local information from the Internet. This paper provides an overview of recommender systems that include collaborative filtering, content-based filtering and hybrid approach of recommender system.
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Product Recommendation Using Social Media to E-Commerce with MicroBlogging: Cold Start

Product Recommendation Using Social Media to E-Commerce with MicroBlogging: Cold Start

In this paper, we have studied a novel problem,cross-site cold-start product recommendation, i.e., recommending products from e-commerce websites to mi- croblogging users without historical purchase records.Our main idea is that on the e-commerce websites,users and products can be represented in the samelatent feature space through feature learning withthe recurrent neural networks. Using a set of linkedusers across both e-commerce websites and social networking sites as a bridge, we can learn feature mapping functions using a modified gradient boostingtrees method, which maps users’ attributes extractedfrom social networking sites onto feature representations learned from e-commerce websites. The mappeduser features can be effectively incorporated into afeature-based matrix factorisation approach for cold-start product recommendation. We have constructed alarge dataset from WEIBOand JINGDONG. The resultsshow that our proposed framework is indeed
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Efficient Prediction of Difficult Keyword Queries using Collaborative Filtering Recommendation Framework

Efficient Prediction of Difficult Keyword Queries using Collaborative Filtering Recommendation Framework

[11] C. Hauff, L. Azzopardi, and D. Hiemstra, ―The combination andevaluation of query performance prediction methods,‖ in Proc.31st ECIR, Toulouse, France, 2009, pp. 301–312. [12] E. Yom-Tov, S. Fine, D. Carmel, and A. Darlow, ―Learning to estimate query difficulty: Including applications to missing content detection and distributed information retrieval,‖ in Proc. 28th Annu. Int. ACM SIGIR Conf. Research Development Information Retrieval, Salvador, Brazil, 2005, pp. 512–519.

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Music Recommendation using Collaborative Filtering and Deep Learning

Music Recommendation using Collaborative Filtering and Deep Learning

Abstract: The concept of filtering out songs based on the interest of a user is the core principle of today's music streaming (MS) service. Recommendation Systems (RS) are a key component of the MS companies. Different companies use different types of RS. Since the web is now an important medium for almost every kind of business and electronic transaction, it serves up as the driving force for the development of RS technology. There is significant dependency that exists between user and item-based activity which is the basic principle of recommendation. With the rise of digital content distribution, people now have access to music collections on an unprecedented scale. Commercial music libraries easily exceed 15 million songs, which vastly exceeds the listening capability of any single person. With millions of songs to choose from, people sometimes feel overwhelmed.Most common RS are designed using the concept of filtering techniques and deal with the count and similarities between the likenesses of the users. Our approach, in this paper, is to enhance the RS by combining the filtering technique with Deep Learning. It will use the traditional filtering technique and use the album art of the song to recommend new songs. The hybrid RS will scan the album art of the song for unique labels.
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Enhanced Job Recommendation System

Enhanced Job Recommendation System

2) Collaborative Filtering Recommendation (CFR): Collaborative filtering recommendation, known as the user- to-user correlation method, finds similar users who have the same taste with the target user and recommends items based on what the similar users like. The key step in CFR is computing the similarities among users. Collaborative filtering recommendation algorithm can be classified into memory-based and model-based [7, 8]. In the memory- based collaborative filtering recommendation, a user-item rating matrix is usually used as the input [9, 10]. Applied in the job recruiting domain, some user behaviors or actions can generate the user-item rating matrix according to the predefined definitions and transition rules. Färber et al. [11] presented an aspect model to produce a rating matrix that assigns assessed values to candidate’s profile using the Expectation Maximization (EM) algorithm. Collaborative Filtering works by building a database of preferences for items by users. For example, a new user, John, is matched against the database to discover neighbors, which are other users who have historically had similar taste to John. Items that the neighbors like are then recommended to John, as he will also probably like them.
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Comparative E-Business Portal for Marketplaces using Hadoop

Comparative E-Business Portal for Marketplaces using Hadoop

Ziyang Jia, Wei Gao, Yuting Yang, and Xu Chen. “User Based Collaborative Filtering for Tourists Attraction Recommendations.” 2015, IEEE International Conference on Computational Intelligence and Communication Technology. The rate of World Wide Web increases in recent years. Internet enables tourists to search and purchase service at any place. Recommendation system are applied for helping tourists to make personalize vacation plans. This recommendation system is capable of generating list of preference attraction for tourists. Collaborative filtering is one of the recommendation system considered to be effectively adapted in tourist’s domain [18]. Xin Li, Guandong Xu, Enhong Chen, and Yu Zong. “Learning recency based comparative choice towards point of interest recommendation.” 2015, Expert System with Application. When GPS enabled in smart phones, the gap between physical and virtual has been reduced. Location Based System Network (LSBN) has also emerged during this period. The LSBN’s allows user to work on Point of Interest (POI’s) for better service by sharing their experience and opinions about the place they have visited such as companies, restaurants, clubs etc. They have aimed to model, user rating and their choices [19].
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Recommender Systems: From Achievements to Requirements

Recommender Systems: From Achievements to Requirements

Recommender Systems were considered important from the research point of view not more than two decades back, yet a lot has been achieved in this particular field. Jie Lu et al. [3] in their work have given detailed information of numerous Recommender Systems that have been developed so far for various application areas along with the various Recommendation approaches. J.Bobadilla et al. [4] in their work discussed briefly about the concept of recommender systems, recommendation approaches and algorithms developed so far with. They also discussed how recommender systems evolved from the very beginning to the most recently developed ones. Lalita Sharma et al. [5] in their work discussed about the most common recommendation approaches of collaborative filtering, content based filtering and hybrid recommender systems. They also gave an insight into the major limitations of these approaches and gave some idea about the possible research areas to work on. Danial Asanov et al. [6] reviewed the traditional and modern recommendation approaches. They also discussed the various challenges faced by Recommender systems. Gediminas Adomavicius et al. [7] gave an overview of Recommender systems and the state-of-the-art. The authors also discussed the most common recommendation approaches, their limitations and what can possibly be done to overcome those limitations.
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User preference tree based personalized online learning managment system

User preference tree based personalized online learning managment system

RACOFI combines two recommendation approaches by integrating a collaborative filtering engine, which works with ratings that users provide for learning resources, with an inference rule engine that is mining association rules between the learning resources and using them for recommendation. The questions sharing and interactive assignments (QSIA) for learning resources sharing, assessing, and recommendation were developed by Rafaeli et al. [2005]. Manouselis et al. [2007] tried to use a typical neighborhood-based set of CF algorithms to support learning object recommendation. Their research considers multidimensional ratings that users provide for learning resources. According the results of this study, it seems that the performance of the same algorithms changes depending on the context where testing takes place. Since, in an e-learning environment, learning resources are provided in a variety of multimedia formats, including text, hypertext, image, video, audio, and slides, it is difficult to calculate the content similarities of two items [Chen, 2012]. Wecan use users’ preference information as a good indication for recommendation in e-learning systems [Yu, 2011]. Regardless of its success in many application domains, collaborative filtering has two serious drawbacks. First, its applicability and quality is limited by the so-called sparsity problem, which occurs when the available data are insufficient for identifying similar users. Therefore, many research works have been run to alleviate the sparsity problem using data mining techniques. For example, Romero et al. [2009] developed a specific web mining tool for discovering suitable rules in a recommender engine. Their objective was to recommend to a student the most appropriate links/WebPages to visit next. Second, it requires knowing many user profiles to elaborate accurate recommendations for a given user. Therefore, in some e- learning environments, that number of learners is low; recommendation results have no adequate accuracy.
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AN EFFICIENT SUPER PEER SELECTION ALGORITHM FOR PEER TO PEER (P2P) LIVE 
STREAMING NETWORK

AN EFFICIENT SUPER PEER SELECTION ALGORITHM FOR PEER TO PEER (P2P) LIVE STREAMING NETWORK

5. CONCLUSION AND FUTURE WORKS In this work, we proposed the diamond recommendation system by using K-Means and Collaborative Filtering techniques based on Mobile Application. This system provides more suitable recommendation information to users. K-means was used to cluster optimal groups and Collaborative filtering produced recommendation results based on user’s voting and preferences. The initial results showed that our approach is successfully generated the recommendation results matching with the group of users. As for the future work, we need to explore more reasonable other technologies to apply in this project to enhance the quality and quantity of services to users.
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