Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   tackle the problem of recommendation in the scenarios with binary relevance data, when only a few (k) items are recommended to individual users. Past work on Collaborative Filtering (CF) has either not addressed the ranking problem for binary relevance datasets, or not specifically focused on improving top-k recommendations. To solve the problem  propose a new CF approach, Collaborative Less-is-More Filtering (CLiMF). In CLiMF the model parameters are learned by directly maximizing the Mean Reciprocal Rank (MRR), which is a ll-known information retrieval metric for capturing the performance of top-k recommendations.  achieve linear computational complexity by introducing a lor bound of the smoothed reciprocal rank metric. Experiments on two social network datasets show that CLiMF significantly outperforms a naive baseline and two state-of-the-art CF methods.
['Yue Shi', 'Alexandros Karatzoglou', 'Linas Baltrunas', 'Martha Larson', 'Nuria Oliver', 'Alan Hanjalic']
I am writing a research paper on a idea CLiMF: collaborative less-is-more filtering
[{"from": "human", "value": "I am writing a research paper on a idea CLiMF: collaborative less-is-more filtering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   tackle the problem of recommendation in the scenarios with binary relevance data, when only a few (k) items are recommended to individual users. Past work on Collaborative Filtering (CF) has either not addressed the ranking problem for binary relevance datasets, or not specifically focused on improving top-k recommendations. To solve the problem  propose a new CF approach, Collaborative Less-is-More Filtering (CLiMF). In CLiMF the model parameters are learned by directly maximizing the Mean Reciprocal Rank (MRR), which is a ll-known information retrieval metric for capturing the performance of top-k recommendations.  achieve linear computational complexity by introducing a lor bound of the smoothed reciprocal rank metric. Experiments on two social network datasets show that CLiMF significantly outperforms a naive baseline and two state-of-the-art CF methods."}]