# Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
## 6 Hybrid Miscellaneous Methods  
In this category of research, multi-modalities and multi-factors are incorporated to recommend hashtags under various assumptions. [PERSON] et al. [12] assumed that _users frequently tweet about topics they are interested in_. Thus, their hashtag recommendation model determined the relationship between the significant terms and hashtags by their shared topics. With 16 predefined class labels (art and design, books, business, etc. ), they classified significant terms in a tweet into one of these class labels using Naive Bayes. To personalize their model, they extracted the significant terms from every user and classified them into one of the predefined class categories. They ranked candidates' hashtags based on the similarity of the terms to the hashtags, user interests, and popularity. They found that the performance of the hashtag recommendation on sports tweets outperformed the performance on tweets in other categories, and the performance on news tweets was the poorest compared to all other categories. SenSim+Ac+Te is a hashtag recommendation model [5] that incorporated three factors: similarity of a hashtag to a tweet, user acceptance degree and the development tendency of a hashtag. They used a modified version of LDA called JGibbLDA as the base of their hashtag recommendation model. They first generated the topical feature vectors of tweets and hashtags. Then, they calculated their similarity. Their research observed that given two hashtags that were different but relevant in the sense that they were about the same topic or event, people usually tended to use the shorter hashtag. Under this observation, they gave the shorter hashtag a higher probability and thus a higher ranking score. They calculated the user acceptance score for a hashtag \(h\) as \(\log(N_{h})\), where \(N_{h}\) was the number of tweets containing the hashtag \(h\). As for the development tendency, they used Polynomial Spline Estimator and Sigmoid function to detect the shift in hashtag trends over seven days. Hashtags with a similar development tendency and higher user acceptance had higher ranks for the recommendation. The linear model and Topic-STG are two personalized hashtag recommendation models introduced by [PERSON] et al. [6]. These two models were based on the assumption that _user behaviors on hashtags present the same meaning as the operations on tweets containing these hashtags_. These operations included the number of'retweet', 'create', 'comment', 'favorite' and 'add friend' actions. They also assumed that _tweets and hashtags with a larger number of operations are popular_ and that this popularity changes over time. According to these two assumptions, in the linear model, they calculated the content similarity of the user interest with the content of hashtags using the negative symmetric Kullback-Leibler divergence where the user's interest is generated using LDA. They also classified hashtags into _timensetitive_ and _time-insensitive_ and calculated the popularity scores for a hashtag. Based on these three variables, they recommended the top-\(k\) hashtags. In the Topic-STG model, they assumed that the user's interest varies over time. On that basis, they proposed a directed and weighted graph-based model of users, topic, hashtags and session time. Using the session time, they determined the user's short- and long-term preferences. Their Topic-STG model outperformed the linear model but has a higher complexity with respect to an increase in the number of topics. It was also difficult to incorporate additional features. [PERSON] et al. [69] incorporated external knowledge from Wikipedia and news articles extracted from the web. [PERSON] and [PERSON] [13] proposed Hybrid+. This optimization-based framework includes features related to tweets (terms, URLs and mentions), users (ID, location and social relation), and hashtags (length, frequency, uptrend and time). They found that a user's interest can change within a week.