Make it personal: A social explanation system applied to group recommendations

Quijano-Sánchez, Lara; Sauer, Christian; Recio-Garcí­a, Juan A.; Dí­az-Agudo, Belén

doi:10.1016/j.eswa.2017.01.045

Cited by 54 publications

(22 citation statements)

References 27 publications

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“…In this framework, a recommendation can be explained based on the target user's friends who have similar preferences, as shown in Figure 2.12. Quijano-Sanchez et al (2017) introduced a social explanation system applied to group recommendation, which significantly increased the likelihood of the user acceptance, the user satisfaction, and the system efficiency to help users make decisions. Wang et al (2014) generates social explanations such as "A and B also like the item".…”

Section: Social Explanationmentioning

confidence: 99%

Explainable Recommendation: A Survey and New Perspectives

Zhang

Chen

2020

FNT in Information Retrieval

526

241

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Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc or directly come from an explainable model (also called interpretable or transparent model in some contexts). Explainable recommendation tries to address the problem of why: by providing explanations to users or system designers, it helps humans to understand why certain items are recommended by the algorithm, where the human can either be users or system designers. Explainable recommendation helps to improve the transparency, persuasiveness, effectiveness, trustworthiness, and satisfaction of recommendation systems. It also facilitates system designers for better system debugging. In recent years, a large number of explainable recommendation approaches -especially model-based methods -have been proposed and applied in real-world systems.In this survey, we provide a comprehensive review for the explainable recommendation research. We first highlight the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why. We then conduct a comprehensive survey of explainable recommendation on three perspectives: 1) We provide a chronological research timeline of explainable recommendation, including user study approaches in the early years and more recent model-based approaches. 2) We provide a two-dimensional taxonomy to classify existing explainable recommendation research: one dimension is the information source (or display style) of the explanations, and the other dimension is the algorithmic mechanism to generate explainable recommendations. 3) We summarize how explainable recommendation applies to different recommendation tasks, such as product recommendation, social recommendation, and POI recommendation.We also devote a section to discuss the explanation perspectives in broader IR and AI/ML research. We end the survey by discussing potential future directions to promote the explainable recommendation research area and beyond.Explainable recommendation refers to personalized recommendation algorithms that address the problem of why -they not only provide users or system designers with recommendation results, but also explanations to clarify why such items are recommended. In this way, it helps to improve the transparency, persuasiveness, effectiveness, trustworthiness, and user satisfaction of the recommendation systems. It also facilitates system designers to diagnose, debug, and refine the recommendation algorithm.To highlight the position of explainable recommendation in the recommender system research area, we classify personalized recommendation with a broad conceptual taxonomy. Specifically, personalized recommendation research can be classified into the 5W problemswhen, where, who, what, and why, corresponding to time-aware recommendation (when), location-based recommendation (where), social recommendation (who), application-aware re...

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Section: Social Explanationmentioning

confidence: 99%

Explainable Recommendation: A Survey and New Perspectives

Zhang

Chen

2020

FNT in Information Retrieval

526

241

View full text Add to dashboard Cite

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“…To get the best recommendation which matches the user request, the users and the providers must cooperate. In [67] propose a Personalized Social Individual Explanation approach ( PSIE ) Which led to improve trustworthiness and users' satisfaction • Gray-Sheep: When the user opinion is not clear or cannot be classified correctly, it is known as gray sheep information which means uncertain value. It's lead to a lack of benefit from this information.…”

Section: B Limitations Of Recommendation Systemmentioning

confidence: 99%

The Role of Trust to Enhance the Recommendation System Based on Social Network

Tharwat

Jacob

Fudzee

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Recommendation systems or recommender system (RSs) is one of the hottest topics nowadays, which is widely utilized to predict an item to the end-user based on his/her preferences primary. Recommendation systems applied in many areas mainly in commercial applications. This work aims to collect evidence of utilizing social network information between users to enhance the quality of traditional recommendation system. It provides an overview of traditional and modern approaches used by RSs such as collaborative filter (CF) approach, content-based (CB) approach, and hybrid filter approach. CF is one of the most famous traditional approaches in RSs, which is facing many limitations due to the lack of information available during a performance such as Cold start, Sparsity and Shilling attack. Additionally, this content focused on the role of incorporating a trust relationship from the social network to enhance the weaknesses of CF and achieve better quality in the recommendation process. Trust-aware Recommendation Systems (TaRSs) is a modern approach proposed to overcome the limitations of CF recommendation system in a social network. The trust relationship between users can boost and enhance CF limitations. Many researchers are focusing on trust in the recommendation system but fewer works are highlighting the role of trust in the recommendation system. In the end, limitations, and open issues of the current picture of the recommendation system come across.

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“…GR is an important application problem in many social activities and industries, such as online shopping, music sharing and group travelling. GR belongs to social services [10], so it inevitably emphasizes the service quality (QoS) [11], and privacy preservation and dynamicity [12]. Service recommendation research has a long history, along with personalized recommendation technology, such as collaborative filtering (CF) [13], matrix decomposition (MF) [14] and deep learning (DL) [15] have been widely studied and the research on group recommendation is still very limited.…”

Section: Group Recommendationmentioning

confidence: 99%

“…Alistair et al [21] conducted relevant research on the role of SBH online and offline social activities in social relationships, and the experiment showed that the satisfaction of group members in social activities increased with the increase in intimacy. Lara Quijano-Sanchez et al [10] gave comprehensive consideration to the satisfaction of the group and the friendly relationship within the group and put forward a personalized group recommendation method (personalized social individual explanation). Yue Ding et al [22] calculated the influence value of users through the social interaction information of users in the group and the similarity relationship between users and then used the regularization classification method to deal with the problem of different preferences among users in the group.…”

Section: Social Recommendationmentioning

confidence: 99%

Group recommendation based on hybrid trust metric

2020

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Group recommendation is a special service type which has the ability to satisfy a group's common interest and find the preferred items for group users. Deep mining of trust relationship between group members can contribute to the improvement of accuracy during group recommendation. Most of the existing trust-based group recommendation methods pay little attention to the diversity of trust sources, resulting in poor recommendation accuracy. To address the problem above, this paper proposes a group recommendation method based on a hybrid trust metric (GR-HTM). Firstly, GR-HTM creates an attribute trust matrix and a social trust matrix based on user attributes and social relationships, respectively. Secondly, GR-HTM accomplishes a hybrid trust matrix based on the integration of these two matrices with the employment of the Tanimoto coefficient. Finally, GR-HTM calculates weights for each item in the hybrid trust matrix based on weighted-meanlist and proceeds to group recommendation with a given trust threshold. Simulation experiments demonstrate that the proposed GR-HTM has better performance for group recommendation in accuracy and effectiveness. ARTICLE HISTORY

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Make it personal: A social explanation system applied to group recommendations

Cited by 54 publications

References 27 publications

Explainable Recommendation: A Survey and New Perspectives

Explainable Recommendation: A Survey and New Perspectives

The Role of Trust to Enhance the Recommendation System Based on Social Network

Group recommendation based on hybrid trust metric

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