Ad Recommendation for Sponsored Search Engine via Composite Long-Short Term Memory

Search engine logs contain a large amount of users' click-through data that can be leveraged as implicit indicators of relevance. In this paper we address ad recommendation problem that finding and ranking the most relevant ads with respect to users' search queries. Due to the click sparsity, the conventional methods can hardly model the both inter- and intra-relations among users, queries and ads. We utilize the long-short term memory(LSTM) network to effectively encode two kinds of sequences: the (user, query) sequence and the query word sequence to represent users' query intention in a continuous vector space and decode them as distributions over ads respectively. Further more, we combine these two LSTM networks in an appropriate way to build up a more robust model referred as composite LSTM model(cLSTM) for ad recommendation. We evaluate the proposed cLSTM on real world click-through data set comparing with two baseline methods, the results demonstrate that our proposed model outperforms two baselines and mitigate the click sparsity problem to a certain degree.