uninteresting and rated items to predict the relative preferences of unrated items more accurately. With the first strategy, because users are aware of the existence of uninteresting items but do not like them, such uninteresting items are likely to be false positives if included in top-N recommendation. Therefore, it is effective to exclude uninteresting items from top-N recommendation results. Next, the second strategy can be interpreted using the concept of typical memory based CF methods. ADVANTAGES OF PROPOSED SYSTEM: We introduce a new notion of uninteresting items, and classify user preferences into pre-use and post-use preferences to identify uninteresting items. We propose to identify uninteresting items via peruse preferences by solving the OCCF problem and show its implications and effectiveness. We propose low-value injection (called l-injection) to improve the accuracy of top-N recommendation in existing CF algorithms. While existing CF methods only employ user preferences on rated items, the proposed approach employs both peruse and post-use preferences. Specifically, the proposed approach first infers pre-use preferences of unrated items and identifies uninteresting items.
uninteresting and rated items to predict the relative preferences of unrated items more accurately. With the first strategy, because users are aware of the existence of uninteresting items but do not like them, such uninteresting items are likely to be false positives if included in top-N recommendation. Therefore, it is effective to exclude uninteresting items from top-N recommendation results. Next, the second strategy can be interpreted using the concept of typical memory based CF methods. ADVANTAGES OF PROPOSED SYSTEM: We introduce a new notion of uninteresting items, and classify user preferences into pre-use and post-use preferences to identify uninteresting items. We propose to identify uninteresting items via peruse preferences by solving the OCCF problem and show its implications and effectiveness. We propose low-value injection (called l-injection) to improve the accuracy of top-N recommendation in existing CF algorithms. While existing CF methods only employ user preferences on rated items, the proposed approach employs both peruse and post-use preferences. Specifically, the proposed approach first infers pre-use preferences of unrated items and identifies uninteresting items.