439 research outputs found
Show Me the Money: Dynamic Recommendations for Revenue Maximization
Recommender Systems (RS) play a vital role in applications such as e-commerce
and on-demand content streaming. Research on RS has mainly focused on the
customer perspective, i.e., accurate prediction of user preferences and
maximization of user utilities. As a result, most existing techniques are not
explicitly built for revenue maximization, the primary business goal of
enterprises. In this work, we explore and exploit a novel connection between RS
and the profitability of a business. As recommendations can be seen as an
information channel between a business and its customers, it is interesting and
important to investigate how to make strategic dynamic recommendations leading
to maximum possible revenue. To this end, we propose a novel \model that takes
into account a variety of factors including prices, valuations, saturation
effects, and competition amongst products. Under this model, we study the
problem of finding revenue-maximizing recommendation strategies over a finite
time horizon. We show that this problem is NP-hard, but approximation
guarantees can be obtained for a slightly relaxed version, by establishing an
elegant connection to matroid theory. Given the prohibitively high complexity
of the approximation algorithm, we also design intelligent heuristics for the
original problem. Finally, we conduct extensive experiments on two real and
synthetic datasets and demonstrate the efficiency, scalability, and
effectiveness our algorithms, and that they significantly outperform several
intuitive baselines.Comment: Conference version published in PVLDB 7(14). To be presented in the
VLDB Conference 2015, in Hawaii. This version gives a detailed submodularity
proo
Regularizing Matrix Factorization with User and Item Embeddings for Recommendation
Following recent successes in exploiting both latent factor and word
embedding models in recommendation, we propose a novel Regularized
Multi-Embedding (RME) based recommendation model that simultaneously
encapsulates the following ideas via decomposition: (1) which items a user
likes, (2) which two users co-like the same items, (3) which two items users
often co-liked, and (4) which two items users often co-disliked. In
experimental validation, the RME outperforms competing state-of-the-art models
in both explicit and implicit feedback datasets, significantly improving
Recall@5 by 5.9~7.0%, NDCG@20 by 4.3~5.6%, and MAP@10 by 7.9~8.9%. In addition,
under the cold-start scenario for users with the lowest number of interactions,
against the competing models, the RME outperforms NDCG@5 by 20.2% and 29.4% in
MovieLens-10M and MovieLens-20M datasets, respectively. Our datasets and source
code are available at: https://github.com/thanhdtran/RME.git.Comment: CIKM 201
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