Hashtag Recommendation for Enterprise Applications - , Dhruv Mahajan, Vishwajit Kolathur; Bansal, Chetan; Sellamanickam, Sundararajan; Gehrke, Johannes, Sathiya Keerthi; Parthasarathy, Suresh (CIKM, 2016)
Hashtags have been popularly used in several social cum consumer network settings such as Twitter and Facebook. In this paper, we consider the problem of recommending hashtags for enterprise applications. These applications include emails (e.g., Outlook), enterprise social networks (e.g., Yammer) and special interest group email lists. This problem arises in an organization setting and hashtags are enterprise domain specific. One important aspect of our recommendation system is that we recommend hashtags for Inline hashtag scenario where recommendations change as the user inserts hashtags while typing the message. This involves working with partial content information. Besides this, we consider the conventional Post] hash tagging scenario where hashtags are recommended for the full message. We also consider an important (sub)scenario, viz., Auto-complete where hashtags are recommended with user provided partial information such as sub-string present in the hashtag. Auto-complete can be used with both Inline and Post scenarios. To the best of our knowledge, Inline, Auto-complete hashtag recommendations and hashtagging in enterprise applications have not been studied before. We propose to learn a joint model that uses features of three types, namely, temporal, structural and content. Our learning formulation handles all the hashtagging scenarios naturally. Comprehensive experimental study on five datasets of user email accounts collected by running an Outlook plugin (a key requirement for large scale industrial deployment), one dataset of special interest group email list and one enterprise social network data set shows that the proposed method performs significantly better than the state of the art methods used in consumer applications such as Twitter. The primary reason is that different feature types play dominant role in different scenarios and datasets. Since the joint model makes use of all feature types effectively, it performs better in almost all scenarios and datasets.