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Recently, deep learning models play more and more important roles in contentsrecommender systems. However, although the performance of recommendations isgreatly improved, the "Matthew effect" becomes increasingly evident. While thehead contents get more and more popular, many competitive long-tail contentsare difficult to achieve timely exposure because of lacking behavior features.This issue has badly impacted the quality and diversity of recommendations. Tosolve this problem, look-alike algorithm is a good choice to extend audiencefor high quality long-tail contents. But the traditional look-alike modelswhich widely used in online advertising are not suitable for recommendersystems because of the strict requirement of both real-time and effectiveness.This paper introduces a real-time attention based look-alike model (RALM) forrecommender systems, which tackles the challenge of conflict between real-timeand effectiveness. RALM realizes real-time look-alike audience extensionbenefiting from seeds-to-user similarity prediction and improves theeffectiveness through optimizing user representation learning and look-alikelearning modeling. For user representation learning, we propose a novel neuralnetwork structure named attention merge layer to replace the concatenationlayer, which significantly improves the expressive ability of multi-fieldsfeature learning. On the other hand, considering the various members of seeds,we design global attention unit and local attention unit to learn robust andadaptive seeds representation with respect to a certain target user. At last,we introduce seeds clustering mechanism which not only reduces the timecomplexity of attention units prediction but also minimizes the loss of seedsinformation at the same time. According to our experiments, RALM shows superioreffectiveness and performance than popular look-alike models.

Real-time Attention Based Look-alike Model for Recommender System