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Hashtag recommendation suggests hashtags to users while they write microblogs in social media platforms. Although researchers have investigated various methods and factors that affect the performance of hashtag recommendations in Twitter and Sina Weibo, a systematic review of these methods is lacking.
2. A New Taxonomy for Hashtag Recommendation of Tweets
Despite the advancement of the current methods, further improvements are required to propose more effective methods that are less expensive in terms of time and computation and provide a personalized recommendation that covers a broader range of pre-defined and novel hashtags with higher accuracy. Furthermore, most of the previous research was tested offline. Recommending personalized hashtags in real-time is more difficult where the recommended hashtags need to be accurate and given instantly.
As an extension to work presented in Alsini et al.’s paper , the association of the four networks and their combined effect on the performance of hashtag recommendation can be examined. In addition, rather than considering the mutual tie relationships between users, weighted relationships can be used to construct the networks and detect communities.
It is challenging to compare newly proposed methods with baseline methods due to the variance in the size of the datasets (i.e., number of tweets, users, and hashtags). It is recommended for future research papers to set a minimum size of the dataset for evaluation.
With the dynamic nature of social media platforms, studies of hashtag recommendation should focus more on the automatic update of the data on the recommendation.
The entry is from 10.3390/fi13050129
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