IMPROVING AGGREGATE RECOMMENDATION DIVERSITY
USING RANKING-BASED TECHNIQUES
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, have often been overlooked. 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. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating datasets and different rating prediction algorithms.
There is a growing awareness of the importance of aggregate diversity in recommender systems. Furthermore, while, as mentioned earlier, there has been significant amount of work done on improving individual diversity, the issue of aggregate diversity in recommender systems has been largely untouched.
It is becoming increasingly harder to find relevant content. This problem is not only widespread but also alarming.
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 (typically using known user ratings and possibly also information about item content or user demographics) 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.
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 (i.e., only on the candidate items of each individual user) without having to keep track of the “global” information, such as which items have been recommended across all users and how many times.
There exist multiple variations of neighborhood-based CF techniques. In this paper, to estimate R*(u, i), i.e., the rating that user u would give to item i, we first compute the similarity between user u and other users u' using a cosine similarity metric. Where I (u, u') represents the set of all items rated by both user u and user u'. Based on the similarity calculation, set N (u) of nearest neighbors of user u is obtained. The size of set N (u) can range anywhere from 1 to |U|-1, i.e., all other users in the dataset.
Then, R*(u, i) is calculated as the adjusted weighted sum of all known ratings R (u', i) Here R (u) represents the average rating of user u. A neighborhood-based CF technique can be user-based or item-based, depending on whether the similarity is calculated between users or items, the user-based approach, but they can be straightforwardly rewritten for the item-based approach because of the symmetry between users and items in all neighborhood-based CF calculations. In our experiments we used both user-based and item-based approaches for rating estimation.
1. POSTING THE OPINION
2. RECOMMENDATION TECHNIQUE
3. RATING PREDICTION
4. RANKING APPROACH
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.
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.
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.
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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