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Despite the somewhat different techniques used in developing search enginesand recommender systems, they both follow the same goal: helping people to getthe information they need at the right time. Due to this common goal, searchand recommendation models can potentially benefit from each other. The recentadvances in neural network technologies make them effective and easilyextendable for various tasks, including retrieval and recommendation. Thisraises the possibility of jointly modeling and optimizing search ranking andrecommendation algorithms, with potential benefits to both. In this paper, wepresent theoretical and practical reasons to motivate joint modeling of searchand recommendation as a research direction. We propose a general framework thatsimultaneously learns a retrieval model and a recommendation model byoptimizing a joint loss function. Our preliminary results on a dataset ofproduct data indicate that the proposed joint modeling substantiallyoutperforms the retrieval and recommendation models trained independently. Welist a number of future directions for this line of research that canpotentially lead to development of state-of-the-art search and recommendationmodels.

Joint Modeling and Optimization of Search and Recommendation