25-04-2012, 12:03 PM
hi can i get the source code for the project 'mining web graphs for recommendations' in java
contact:
Y.Hari Prakash
+919944116770
hariprakash.y[at]gmail.com
25-04-2012, 12:03 PM
hi can i get the source code for the project 'mining web graphs for recommendations' in java contact: Y.Hari Prakash +919944116770 hariprakash.y[at]gmail.com
28-09-2012, 01:40 PM
even i'm searching the same... i've some downloaded materials reg this project.. can i send?
09-10-2012, 09:21 PM
I can do this project in java(Mining Web Graphs for Recommendations)
contact me..addhinu[at]gmail.com
10-10-2012, 11:15 AM
to get information about the topic "mining web graphs for recommendations" full report ppt and related topic refer the link bellow
https://seminarproject.net/Thread-mining...ons--41522 https://seminarproject.net/Thread-mining...ations-ppt
21-12-2012, 05:51 PM
Mining Web Graphs for Recommendations Mining Web Graphs.pdf (Size: 1.78 MB / Downloads: 30) Abstract As the exponential explosion of various contents generated on the Web, Recommendation techniques have become increasingly indispensable. Innumerable different kinds of recommendations are made on the Web every day, including movies, music, images, books recommendations, query suggestions, tags recommendations, etc. No matter what types of data sources are used for the recommendations, essentially these data sources can be modeled in the form of various types of graphs. In this paper, aiming at providing a general framework on mining Web graphs for recommendations, (1) we first propose a novel diffusion method which propagates similarities between different nodes and generates recommendations; (2) then we illustrate how to generalize different recommendation problems into our graph diffusion framework. The proposed framework can be utilized in many recommendation tasks on the World Wide Web, including query suggestions, tag recommendations, expert finding, image recommendations, image annotations, etc. The experimental analysis on large datasets shows the promising future of our work. INTRODUCTION With the diverse and explosive growth of Web information, how to organize and utilize the information effectively and efficiently has become more and more critical. This is especially important for Web 2.0 related applications since usergenerated information is more free-style and less structured, which increases the difficulties in mining useful information from these data sources. In order to satisfy the information needs of Web users and improve the user experience in many Web applications, Recommender Systems, have been well studied in academia and widely deployed in industry. Typically, recommender systems are based on Collaborative Filtering [14], [22], [25], [41], [46], [49], which is a technique that automatically predicts the interest of an active user by collecting rating information from other similar users or items. The underlying assumption of collaborative filtering is that the active user will prefer those items which other similar users prefer [38]. Based on this simple but effective intuition, collaborative filtering has been widely employed in some large, well-known commercial systems, including product recommendation at Amazon1, movie recommendation at Netflix2, etc. Typical collaborative filtering algorithms require a user-item rating matrix which contains user-specific rating preferences to infer users’ characteristics. However, in most of the cases, rating data are always unavailable since information on the Web is less structured and more diverse. RELATED WORK Recommendation on the Web is a general term representing a specific type of information filtering technique that attempts to present information items (queries, movies, images, books, Web pages, etc.) that are likely of interest to the users. In this section, we review several work related to recommendation, including collaborative filtering, query suggestion techniques, image recommendation methods, and clickthrough data analysis. Collaborative Filtering Two types of collaborative filtering approaches are widely studied: neighborhood-based and model-based. The neighborhood-based approaches are the most popular prediction methods and are widely adopted in commercial collaborative filtering systems [37], [47]. The most analyzed examples of neighborhood-based collaborative filtering include user-based approaches [7], [21] and item-based approaches [15], [37], [50]. User-based approaches predict the ratings of active users based on the ratings of their similar users, and item-based approaches predict the ratings of active users based on the computed information of items similar to those chosen by the active user. User-based and itembased approaches often use the PCC (Pearson Correlation Coefficient) algorithm [47] and the VSS (Vector Space Similarity) algorithm [7] as the similarity computation methods. PCC-based collaborative filtering generally can achieve higher performance than the other popular algorithm VSS, since it considers the differences of user rating style. Query Suggestion In order to recommend relevant queries to Web users, a valuable technique, query suggestion, has been employed by some prominent commercial search engines, such as Yahoo!3, Live Search4, Ask5 and Google6. However, due to commercial reasons, few public papers have been released to reveal the methods they adopt. The goal of query suggestion is similar to that of query expansion [11], [13], [56], [61], query substitution [31] and query refinement [35], [57], which all focus on understanding users’ search intentions and improving the queries submitted by users. Query suggestion is closely related to query expansion or query substitution, which extends the original query with new search terms to narrow down the scope of the search. But different from query expansion, query suggestion aims to suggest full queries that have been formulated by previous users so that query integrity and coherence are preserved in the suggested queries [18]. Query refinement is another closelyrelated notion, since the objective of query refinement is interactively recommending new queries related to a particular query. Image Recommendation Besides query suggestion, another interesting recommendation application on the Web is image recommendation. Image recommendation systems, like Photoree8, focus on recommending interesting images to Web users based on users’ preference. Normally, these systems first ask users to rate some images as they like or dislike, and then recommend images to the users based on the tastes of the users. In the academia, few tasks are proposed to solve the image recommendation problems since this is a relatively new field and analyzing the image contents is a challenge job. Recently, in [63], by employing the Flickr dataset, Yang et al. proposed a context-based image search and recommendation method to improve the image search quality and recommend related images and tags. However, since it is a context-based method, the computational complexity is very high and it cannot scale to large datasets. While in our framework proposed in this paper, by diffusing on the imagetag bipartite graph with one or more images, we can accurately and efficiently suggest semantically relevant non-personalized or personalized images to the users. Diffusion on Directed Graphs The above heat diffusion model is designed for undirected graphs, but in many situations, the Web graphs are directed, especially in online recommender systems or knowledge sharing sites. Every user in knowledge sharing sites typically has a trust list. The users in the trust list can influence this user deeply. These relationships are directed since user a is in the trust list of user b, but user b might not be in the trust list of user a. At the same time, the extent of trust relations is different since user ui may trust user uj with trust score 1 while trust user uk only with trust score 0.2. Hence, there are different weights associated with the relations. Based on this consideration, we modify the heat diffusion model for the directed graphs as follows. CONCLUSION In this paper, we present a novel framework for recommendations on large scale Web graphs using heat diffusion. This is a general framework which can basically be adapted to most of the Web graphs for the recommendation tasks, such as query suggestions, image recommendations, personalized recommendations, etc. The generated suggestions are semantically related to the inputs. The experimental analysis on several large scale Web data sources shows the promising future of this approach. |
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