07-04-2012, 07:42 PM
I am Shashank and i have to present a seminar on 'mining web graphs for recommendation'. I need the ppt of above topic as soon as possible..its urgent...thanks.
07-04-2012, 07:42 PM
I am Shashank and i have to present a seminar on 'mining web graphs for recommendation'. I need the ppt of above topic as soon as possible..its urgent...thanks.
02-03-2013, 06:12 PM
send me the ppt related to mining web graphs for recommendation
04-03-2013, 11:08 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...mendations https://seminarproject.net/Thread-mining...ations-ppt
20-07-2013, 04:40 PM
Mining Web Graphs for Recommendations
Mining Web Graphs .docx (Size: 44.93 KB / Downloads: 33) 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 music, images, books recommendations, query suggestions, etc. No matter what types of data sources are used for the recommendations, essentially these data sources can be modeled in the form 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 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, image recommendations, etc. The experimental analysis on large datasets shows the promising future of our work. Existing System: The last challenge is that it is time-consuming and inefficient to design different recommendation algorithms for different recommendation tasks. Actually, most of these recommendation problems have some common features, where a general framework is needed to unify the recommendation tasks on the Web. Moreover, most of existing methods are complicated and require tuning a large number of parameters. Proposed System: In order to satisfy the information needs of Web users and improve the user experience in many Web applications, Recommender Systems. This 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 the proposed method consists of two stages: generating candidate queries and determining “generalization/specialization” relations between these queries in a hierarchy. The method initially relies on a small set of linguistically motivated extraction patterns applied to each entry from the query logs, then employs a series of Web-based precision-enhancement filters to refine and rank the candidate attributes. Posting The Opinion: In this module, we get the opinions from various people about business, e-commerce and products through online. The opinions may be of two types. Direct opinion and comparative opinion. Direct opinion is to post a comment about the components and attributes of products directly. Comparative opinion is to post a comment based on comparison of two or more products. The comments may be positive or negative. Image Recommendation Technique: Another interesting recommendation application on the Web is image recommendation. 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. However, the quality of recommendations can be evaluated along a number of dimensions, and relying on the accuracy of recommendations alone may not be enough to find the most relevant items for each User, these studies argue that one of the goals of recommender systems is to provide a user with highly personalized items, and more diverse recommendations result in more opportunities for users to get recommended such items. With this motivation, some studies proposed new recommendation methods that can increase the diversity of recommendation sets for a given individual user. They can give the feedback of such items. Rating Prediction: First, the ratings of unrated items are estimated based on the available information (typically using known user ratings and possibly also information about item content) using some recommendation algorithm. Heuristic techniques typically calculate recommendations based directly on the previous user activities (e.g., transactional data or rating values). For each user, ranks all the predicted items according to the predicted rating value ranking the candidate (highly predicted) items based on their predicted rating value, from lowest to highest (as a result choosing less popular items.
28-04-2017, 10:32 AM
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