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IMPROVING AGGREGATE RECOMMENDATION DIVERSITY USING RANKING-BASED TECHNIQUES : 

IMPROVING AGGREGATE RECOMMENDATION DIVERSITY USING RANKING-BASED TECHNIQUES Under the Guidance of Shilpa Presented By G.SINDU(09-529) B.VEENA(09-511) A.VIJAY(09-502) G.NAVEEN(09-527)

INTRODUCTION:: 

INTRODUCTION: Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy, other important aspects of recommendation quality, such as the diversity of recommendations. In this paper, we introduce and explore a number of item ranking techniques that can generate recommendations that have substantially higher aggregate diversity across all users while maintaining comparable levels of recommendation accuracy.

Architecture:: 

Architecture: ARCHITECTURE:

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EXISTING SYSTEM: There is a growing awareness of the importance of aggregate diversity in recommender systems. Furthermore, there has been significant amount of work done on improving individual diversity, the issue of aggregate diversity in recommender systems has been largely untouched. DISADVANTAGES: It is becoming increasingly harder to find relevant content. This problem is not only widespread but also alarming.

PROPOSED SYSTEM:: 

PROPOSED SYSTEM: In real world settings, recommender systems generally perform the following two tasks in order to provide recommendations to each user. First, the ratings of unrated items are estimated based on the available information using some recommendation algorithm. And second, the system finds items that maximize the user’s utility based on the predicted ratings, and recommends them to the user. Ranking approaches proposed in this paper are designed to improve the recommendation diversity in the second task of finding the best items for each user.

ADVANTAGES:: 

ADVANTAGES: In particular, these techniques are extremely efficient, because they are based on scalable sorting-based heuristics that make decisions based only on the “local” data without having to keep track of the “global” information.

Recommendation Algorithms:: 

Recommendation Algorithms: Recommendation Techniques for Rating Prediction Accuracy of Recommendations : statistical accuracy metrics, mean absolute error (MAE) and root mean squared error (RMSE) metrics measure how well a system can predict an exact rating value for a specific item. Diversity of Recommendations : the diversity of recommendations can be measured in two ways: individual and aggregate. Most of recent studies have focused on increasing the individual diversity, which can be calculated from each user’s recommendation list.

Recommendation Techniques for Rating Prediction:: 

Recommendation Techniques for Rating Prediction: Recommender systems are usually classified into three categories based on their approach to recommendation: Contentbased:recommend items similar to the ones the user preferred in the past. Collaborative: recommend items that users with similar preferences (i.e., “neighbours”) have liked in the past. hybrid approaches: can combine content-based and collaborative methods in several different ways. Recommender systems can also be classified based on the nature of their algorithmic technique into heuristic (or memory-based) and model based approaches.

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Heuristic technique: Neighborhood based CF technique Model based technique: Matrix factorization CF technique R*(u,i)=P u T qi

RANKING APPROACHES: 

RANKING APPROACHES predicted rating value itself as an item ranking criterion: Reverse Predicted Rating Value : ranking the candidate (highly predicted) items based on their predicted rating value, from lowest to highest. rankRevPred(i) = R*(u,i) Item Average Rating : ranking items according to an average of all known ratings for each item. Item Absolute Likeability: ranking items according to how many users liked them (i.e., rated the item above TH): rankAbsLike(i) = |UH(i)|, where UH(i)={u€U(i)| R(u,i)≥ TH}.

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Item Relative Likeability : ranking items according to the percentage of the users who liked an item (among all users who rated it): rankRelLike(i) = |UH(i)| / |U(i)|. Item Rating Variance : ranking items according to each item’s rating variance (i.e., rating variance of users who rated the item) . Neighbours Rating Variance : ranking items according to the rating variance of neighbours of a particular user for a particular item. The closest neighbours of user u among the users who rated the particular item i, denoted by u', are chosen from the set of U(i)∩N(u).

MODULES:: 

MODULES: POSTING THE OPINION RECOMMENDATION TECHNIQUE RATING PREDICTION RANKING APPROACH

POSTING THE OPINION:: 

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.

RECOMMENDATION TECHNIQUE:: 

RECOMMENDATION TECHNIQUE : 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.

RATING PREDICTION:: 

RATING PREDICTION : First, the ratings of unrated items are estimated based on the available information using some recommendation algorithm. Heuristic techniques typically calculate recommendations based directly on the previous user activities. 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.

RANKING APPROACH:: 

RANKING APPROACH: Ranking items according to the rating variance of neighbors of a particular user for a particular item. There exist a number of different ranking approaches that can improve recommendation diversity by recommending items other than the ones with topmost predicted rating values to a user. A comprehensive set of experiments was performed using every rating prediction technique in conjunction with every recommendation ranking function on every dataset for different number of top- N recommendations.

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SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS: System : Pentium IV 2.4 GHz. Hard Disk : 40 GB. Floppy Drive : 1.44 Mb. Monitor : 15 VGA Colour. Mouse : Logitech. Ram : 512 Mb. SOFTWARE REQUIREMENTS: Operating system : Windows XP. Coding Language : ASP.Net with C# Database : Sql Server 2005.

CONCLUSION:: 

CONCLUSION: Ranking recommendations according to the predicted rating values provides good predictive accuracy. Provides flexibility to system designers to be used in conjunction with different rating prediction algorithms. They are also based on scalable sorting based heuristics and are extremely efficient. Finally, exploration of recommendation diversity when recommending item bundles or sequences also constitute interesting topics for future research.

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THANK YOU