Recommending social media content to community owners

Abstract

Online communities within the enterprise offer their leaders an easy and accessible way to attract, engage, and influence others. Our research studies the recommendation of social media content to leaders (owners) of online communities within the enterprise. We developed a system that suggests to owners new content from outside the community, which might interest the community members. As online communities are taking a central role in the pervasion of social media to the enterprise, sharing such recommendations can help owners create a more lively and engaging community. We compared seven different methods for generating recommendations, including content-based, memberbased, and hybridization of the two. For member-based recommendations, we experimented with three groups: owners, active members, and regular members. Our evaluation is based on a survey in which 851 community owners rated a total of 8,218 recommended content items. We analyzed the quality of the different recommendation methods and examined the effect of different community characteristics, such as type and size.

Sentiment Enhanced Multi-Modal Hashtag Recommendation for Micro-Videos

“…As manually labeled tags for marking topics and emphasizing target content, hashtags have proven to be useful in many applications, including sentiment analysis [13], content recommendation [14], [15], and even been used as the manual supervision and annotation for training vision models [16]. Hashtag recommendation has gained great attention in the field of text and image from different perspectives.…”

CSD: A multi-user similarity metric for community recommendation in online social networks

“…By considering the collective features that may determine users' choices within a community, Hu et al (2014) propose a joint community recommendation model which accommodates both users' individual interests and community decision. Various methods, including content-based, user-based and hybrid of content and user, for producing recommendations for a community of users are examined and compared by Ronen et al (2014). These sophisticated algorithms and models concentrate on how to select the items for a given community of users, whereas it may work inefficiently if the users in a community do not share many interests.…”

Who Wants to Join Me?

“…Group recommendation methods [45,29,3,33,52] aggregate the preferences of a group of users and seek to recommend venues that are most suitable for the group as a whole. For example, Yuan et al, [45] propose a generative model that studies different influences for users in a group.…”

“…For example, two users may be close friends but enjoy very different types of music, in which case they should not be recommended to each other when planning to attend a concert. Companion recommendation is furthermore different from the task of group recommendation [35,45,29,3,33], which also involves multiple users, but where the aim is to recommend the most satisfactory venue to a group of users. Finally, the proposed companion recommendation tasks is also clearly different from POI recommendation.…”