Query Recommendation Using Topic Modeling and Word Embeddings

Query recommendation plays an important role in improving users' search experience. Traditional ways most mine recommended words from log information. However, in user logs, sessions are difficult to divide. At the same time, click results are with bias and noise, and many queries lack clicks, it will make useful information be sparse. In this paper, we present a novel method based on local documents. Different from the traditional query recommendation, this method recommends related terminology according to the meaning of the query. We extract terminology documents from the pseudo-related feedback documents, then model topics of the terminology documents and use the inference strategies to infer the topic of the query to solve the problem of theme drift. In addition, to bring better recommendation results, we fuse supervised and unsupervised methods to mine semantic concept relations between query words and recommended words. Finally, the words with semantic concepts relation are recommended to the user. Experimental results show that our method can meet the user's search needs better. Compared with traditional query recommendation, users prefer the query recommendation way that we propose.