ILCR: Item-based Latent Factors for Sparse Collaborative Retrieval
This work addresses sparsity challenges in collaborative retrieval for applications like search and recommendation, but it is incremental as it builds on existing methods by incorporating item-based information and optimizing with BPR.
The paper tackles the problem of collaborative retrieval, which involves recommending items to users based on queries, by proposing an item-based latent factor model to handle sparsity in query-user-item interactions. The results show improved efficiency and effectiveness on real-world datasets like Last.fm and Yelp.
Interactions between search and recommendation have recently attracted significant attention, and several studies have shown that many potential applications involve with a joint problem of producing recommendations to users with respect to a given query, termed $Collaborative$ $Retrieval$ (CR). Successful algorithms designed for CR should be potentially flexible at dealing with the sparsity challenges since the setup of collaborative retrieval associates with a given $query$ $\times$ $user$ $\times$ $item$ tensor instead of traditional $user$ $\times$ $item$ matrix. Recently, several works are proposed to study CR task from users' perspective. In this paper, we aim to sufficiently explore the sophisticated relationship of each $query$ $\times$ $user$ $\times$ $item$ triple from items' perspective. By integrating item-based collaborative information for this joint task, we present an alternative factorized model that could better evaluate the ranks of those items with sparse information for the given query-user pair. In addition, we suggest to employ a recently proposed scalable ranking learning algorithm, namely BPR, to optimize the state-of-the-art approach, $Latent$ $Collaborative$ $Retrieval$ model, instead of the original learning algorithm. The experimental results on two real-world datasets, (i.e. \emph{Last.fm}, \emph{Yelp}), demonstrate the efficiency and effectiveness of our proposed approach.