ADVANCED ROUTE RECOMMENDATION SYSTEM
1D. Viji, 2Shubham Agrawal, 3Parth Suhane 1
Assistant Professor, 2,3UG Student,
Department of CSE, SRM Institute of Science & Technology, Kattankulathur. 1[email protected], 2[email protected]
3
Abstract: With the increasing popularity of online maps, day by day people are demanding accurate and more efficient systems. So, we aim to provide the user with the system as above-mentioned characteristics. We used "travel" as the part of research and saw that generally, prior works relate to providing rating and ranking of indexes of particular location but our paper provides a rating of a route which would help the user to plan a trip accordingly as per their choices. This would be more efficient instead of ranking or rating a particular place. This work comprises of data mining and its concepts which include mining of keywords after their extraction and then ranking the route. This keyword extraction is used when the people who traveled the route previously and reviewed it. So, finally, in this paper, we propose an advanced travel route recommendation system where the reviews are taken from the user about the route and they also provide a rating to the route based on their experience. This data is collected and is shown to another user (if requested for the same route). To provide better results routing algorithm is used. The routing algorithm is used to pinpoint the location with the help of Google map and also helps in finding the location. The keywords from the reviews of the user are extracted and are explored using spatial keyword query. The spatial-keyword query is used to combine the related words with one another and process the data according to it [1].
Index Terms: Location-based, spatial keyword.
1. Introduction
Today the world is moving very fast and to compete with this new era, the system has to be more efficient and more accurate. So, we thought to redefine the world by making the most efficient part of the daily life “TRAVEL” with more efficiency.
On the basis of the reviews, we recommend routes to people and rank them. As we have seen in prior references that generally instead of taking the route as a system, researchers work on particular latitude and longitude or in simple words they are mainly focused on particular place.
So, to build something unique we thought to recommend a route instead of a particular place.
On the technical side, we used data mining concepts such as keyword extraction and maps to provide results according to it. We ranked our system as per the people reviews who visited the route before and hence recommending result of all the routes from source to destination and finally recommending the best route. As the user travels through the route they share the reviews of the route with their friends on our system. In particular, these reviews are safely collected and used by our system and are mined. As a result, we will be getting massive types of routes with their reviews, which play an essential role in many well-established research areas, such as density or sparsity prediction, smart city planning, traffic management and many more activities. In this paper, we focus on trip planning and route recommendation and our main intention are to discover travel experiences from shared data. To facilitate route planning, the paper provides an interface in which a user could submit the query region (source to destination) and a scenario is considered where users specify their preferences with keywords. For example, “less traffic”, “good restaurants”, “better roads”, “best place to visit” etc.
2. Related Work
The compelling locations and the sequences of travel are mined through the GPS trajectories set up by other users. It provides better presentation ability and also improves the ranking performance. It has low efficiency of sequence mining and also lacks in clustering of users on certain areas [2].
The BKC query is used in place of mCK query which provides sensible decision making by adding an extra dimension. A new algorithm, called keyword-NNE algorithm is proposed here. The keyword-keyword-NNE algorithm is applied on different processing strategy therefore results in reduction of number of candidate keyword covers. Also, after processing each keyword, the keyword-NNE algorithm generates less keyword covers than the keyword covers generated by the baseline algorithm [3].
A new index named Spatial Inverted Index is presented to deal with the top-k spatial keyword queries efficiently. Also, algorithms for single keyword queries and multiple keyword queries are used to improve efficiency. The algorithms introduced were SKA (Single Keyword Query) and MKA (Multiple Keyword Query). To improve the performance of the top-k spatial keyword queries a new novel index named Spatial Inverted Index (S2I) is proposed. The update cost in this is less [4].
The author of the paper presented a new problem relating to getting a cluster of spatial objects, where each object is related to a set of keywords such that it covers the query’s keywords and has the lowest cost. The results show the efficiency and accuracy of algorithms. The flaw here is to know the extent up to which top k groups should be overlapped [5].
A new algorithm, called incremental algorithm, is used which provided better results as compared to the k-nearest neighbour algorithm. In future the author aims to compare the behaviour of the algorithm on spatial data structures. Also, the algorithm wasn’t tested on very large data sets and in high dimensional spaces [6].
A new framework, called as instance optimality framework, is introduced to study certain algorithms like the Threshold Algorithm and algorithms used in scenarios where random access is not possible. This framework analyses and compares the performance of the algorithms [7].
The author uses the skyline concepts to address the problem of trip planning. In order to fetch the travel route with various test areas and a set of test points, a framework is being developed and is called as PASTR (Pattern Aware Skyline Travel Route). For the travel route guidance the skyline is fitted instead of classical organisation. The travel routes are provide based on attractiveness of Region of Interests contained, the distance to the set of query points, and visiting time of Region of Interests. [10].
The k best-connected routes are searched in the directory by using a set of locations. Multiple algorithms were verified and were compared based on their performance, efficiency and memory consumption. The advantages of M and DF-D-M-O were applied to improve the DF-C algorithm which has low memory consumption but lacks in efficiency [19].
3. Proposed System
As we discussed above the increasing population and we need to be more efficient. In this new era, it is of the utter importance to save time and money. So, we thought to save the time in traveling and to recommend route on the basis of some of the attribute as be discussed below. The paper presents and demonstrate that with the help of the existing framework we will be
able to retrieve travel routes that are interesting for users, and will surpass different algorithms in terms of effectiveness and efficiency.
Whenever anyone’s Smartphone's GPS is on, the reviews of the location they are present are taken and recorded in the database which is used for giving recommendations.
In the existing system, the rating was given to locations such as hotels, shops, restaurants etc. Recommendations of only the top-rated locations are given. Instead of giving preference to a specific location, we will be giving a rating to routes between the source and the destination and with the help of these ratings various routes are recommended with attributes including hotels and restaurants. All the routes are presented to the user which exist between the source and the destination and on the basis of reviews of these routes are given a rating and been recommended to people.
In the proposed system, the system deals with real-time databases which help us to propose are system accordingly. Firstly, we implemented the Google maps through API parsing and then the source and the destination are mined from all the reviews given by the people who already visited the routes and are classified as spatial keywords present in reviews. From
there routes the route which starts from the source and end at the destination are chosen and vice versa are eliminated.
Now, these routes source and destination are pinpointed on the map along with reviews of the routes where different attributes are taken. This can be hotel, restaurants, stationary shop etc.
Figure 1. Route System Architecture
In Fig. 1, User Set include the common people, it can be anyone who would visit our system for checking
route. From the user set, only those people seek the permission who will be authorized part of our database. In simple words, the person needs to log in the system to access or to use the system.
Active User is the person who provides various
reviews when the user visits the location. When the GPS of a person’s device is on, the review of the locations and the route are taken from the user
This information is sent to the User Information
Handler where the reviews of the routes are properly
processed and are cleaned according to the format in which it has to be sorted.
All these recommendations are put together properly and are stored in the database for the further recommendation giving purpose. Along with storing in the database, a metadata is also created which is an index of the data in the database.
Whenever a person asks for the route between the source to the destination all the routes are recommended and the reviews are mined and particular locations are pinpointed on the map. From the map, the required routes are picked (we provide the facility of selecting routes on the basis of names and by latitude and longitude) from the Database with the help of
Location Information Handler. Then these routes are
then recommended to the user with a short description of the route and also a rank is provided so that user can choose it according to their convenience.
The above architecture comprises of all the modules and is the work flow of our system where the route has been searched and then the route is recommended on the basis of reviews this will be proved to be more advanced system then the prior works and would be more efficient and accurate in terms of both time and space.
Table 1.Comparison of Different Algorithms & Their Efficiencies
Title Algorithm Used Advantage Disadvantage Accuracy Searching trajectories by locations: An efficiency study [19] IKNN Algorithm Best query performance
High memory usage i. There is no change in Query Time and Node Access as best-first-Optimization offers a very stable performance because there is increase in k from 1 to 25, while the Queue Size is maintained at a comparatively low number (≈ 200). Mining Cluster-based Mobile Sequential Patterns in Location-Based Service Environments [15] Cluster-based Mobile Sequential Pattern Mine High clustering quality and highly precise results
Only moving paths and user clusters are done
i. CO-Smart-CAST achieves high clustering quality.
ii. KNN based sampling strategy CBSS obtains highly precise results for user classification. Learning Points and Routes to Recommend Trajectories [16] Rank +Markov: recommend trajectory performance measure is increased It shows specific initial and final positions of tours. i. increase in evaluations Selecting Stars: The k- Most Representative Skyline Operator [17] top-k representative skyline points Efficiency is more, scalable randomized algorithm with a theoretical accuracy guarantee
For the dimension (dim greater than equals to 3) as well as greedy heuristic for the set envelope issue is instantly tested in order to serve the resemblance ratio 1-1e for the NP hard problem defined for the space.
i. Time Efficient ii. Highly Accurate iii. Space Efficient
LCARS: A Location-Content-Aware Recommender System [18] location-content-aware recommender system, LCARS Facilitate people traveling not only in their native location but also in a new location. Only shows performance where k is in range[1-20]. Performance disparity in top k call.
i. the efficiency of our approach is improved using scalable query processing technique
4. Algorithm
A. Spatial Keyword
The spatial-keyword search is one of the most considerable and famous algorithms by the research community. Some prior works determines about extracting keywords and ranking them as based on the below given example.
Spatial keywords can be categorized into many types and this can be keywords which describe sadness, angriness, happiness and many other emotions. The similarities between documents are measured and applied to the two sets of keywords.
Different varieties of spatial algorithm are being suggested by researchers but here spatial keyword optimizes the
construction and increase the efficiency of indexes used in keyword queries.
The content used for querying takes the form of spatial database. For example, hotels or restaurants stationary shops etc. Suppose if we take an example of a database P which consists of the set of keywords. It extracts the spatial-keyword such as “good”, “better”, “best” and then uses the keyword accordingly. It generally ranks the keyword and accordingly it is being used in our project. Spatial keyword algorithm plays a major role in our project as the reviews are taken on basis of extraction of keywords only. We also ranked keyword such as
“good=1”, “better=2”, “best=3” and as per it marks get improved.
B. Routing Algorithm
Routing algorithm has become the most inevitable part of our daily life. There are many sets of algorithms and these comprise of the set of optimal routes from all routes. Routing algorithm can mainly be categorized into to two types static routing and dynamic routing. Both the algorithms work in a different manner because of the difference in the rules. For eg. static routing algorithm comprise the shortest path between the pair of nodes. The various algorithms which help in finding the shortest path as follows
a) Shortest Path Routing b) Dijkstra Algorithm c) Uses topology d) Greedy approach
e) Possible shorter path of equal length [1, 3, 8, 9] Routing Algorithm is very efficient and is used to pinpoint the location. It is used in Google maps and also helps in finding the current location and helps in finding the shortest path from source to destination. Routing algorithm generally takes 2 attributes such as latitude and longitude.
5. Conclusion & Future Work
The paper briefs and provides a recommendation of the route on the basis of reviews. We used efficient algorithms, application programming interfaces and data mining concepts to provide more efficiency as compared to the prior works undertaken. The efficiency is also improved with respect to time and space complexity. Even real-time datasets are used which helped to understand the real-world difficulties and solve it. The work includes data mining concepts such as keyword extraction which is more accurate than the algorithm which is generally used in prior works. Our paper is efficient to retrieve routes that are interesting for users, and is effective in terms of both efficiency and accuracy. In the paper not only reviews are taken but they are mined and a specific rating is given on the basis of it. Due to the real-time requirements for online systems, the computation cost is reduced by the efficiency of the algorithm. Since new technologies are emerging day by day with new algorithm and techniques these can able to surpass this efficiency and accuracy. Future work can be expanded to recommending the route without extracting keywords and with other methodologies and concepts.
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