# Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
## 8 Conclusions and Future Research Directions  
This paper discusses how hashtag recommendation systems for tweets have evolved within the field of online social networks. It also presents a new taxonomy for hashtag recommendation of tweets based on their methodologies. The taxonomy classifies hashtag recommendation methods for tweets into three main categories: text-based, hybrid user-based and hybrid miscellaneous methods. Text-based methods find hashtags similar to what a user intends to adopt based on the textual information. This category is further classified into tweet-similarity-based methods, probabilistic methods, classification based methods, graph-based methods and matrix factorization based methods. Since methods of collaborative filtering suffer from the cold-start problem, they are integrated with other methods. Hybrid user-based hashtag recommendation methods recommend hashtags based on the similarity of the users' behavior, interests or relations. This category is further classified into behavioral and social collaborative filtering methods. Hybrid miscellaneous hashtag recommendation take advantage of multi-modalities and multi-factors to recommend the hashtags. Regardless of the specific techniques employed, it has become clear that the best outcome can be achieved using the hybrid methods (user-based or miscellaneous) for their ability to overcome problems occurring with content-based and collaborative filtering methods. It was noticed that understanding various factors that affect the performance of hashtag recommendation and the underlying assumptions have a significant impact on the algorithmic approach that should be considered. This subsection also addresses the research question RQ5. We highlight some open challenges, which can be considered future research directions. These challenges are as follows:  
* 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 [PERSON] et al. 's paper [23], 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 relationshipsbetween 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. * Accuracy-based metrics were the primary measures of evaluation for a long time. In recent years, _concepts of evaluation_, which are metrics beyond accuracy, have been studied to evaluate the value of the traditional recommendations. For example, _diversity_ is concerned with the variety of items recommended by the system, and _novelty_ is concerned with how the recommended items are new to users [81; 82]. However, concepts of the evaluation were rarely used to evaluate hashtag recommendation methods. The value of the recommendations also needs to be studied in terms of user satisfaction and expectation. * 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. Conceptualization, A.A. and D.H.; methodology, A.A.; formal analysis, A.A.; data curation, A.A.; writing--original draft preparation, A.A.; writing--review and editing, A.A., D.H. and A.D.; supervision, A.D. and D.H.; project administration, A.D.; All authors have read and agreed to the published version of the manuscript.