A Survey of Explanations in Recommender Systems

Tintarev, Nava; Masthoff, Judith

doi:10.1109/icdew.2007.4401070

Cited by 410 publications

(300 citation statements)

References 26 publications

Supporting

Mentioning

263

Contrasting

Unclassified

Order By: Relevance

“…Explanations play an important role in recommender systems since they increase the trust of users in decision outcomes (Tintarev and Masthoff 2007). In the healthy food domain, explanations are even more necessary since they not only increase the trust in recommendations but also stimulate users to consume healthy foods or change their eating behaviors.…”

Section: Challenges Regarding Explanationsmentioning

confidence: 99%

An overview of recommender systems in the healthy food domain

et al. 2017

View full text Add to dashboard Cite

Recently, food recommender systems have received increasing attention due to their relevance for healthy living. Most existing studies on the food domain focus on recommendations that suggest proper food items for individual users on the basis of considering their preferences or health problems. These systems also provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Also, group recommendation functionalities are very useful in the food domain, especially when a group of users wants to have a dinner together at home or have a birthday party in a restaurant. Such scenarios create many challenges for food recommender systems since the preferences of all group members have to be taken into account in an adequate fashion. In this paper, we present an overview of recommendation techniques for individuals and groups in the healthy food domain. In addition, we analyze the existing state-of-the-art in food recommender systems and discuss research challenges related to the development of future food recommendation technologies.

show abstract

Section: Challenges Regarding Explanationsmentioning

confidence: 99%

An overview of recommender systems in the healthy food domain

et al. 2017

View full text Add to dashboard Cite

show abstract

“…There is an increasing awareness in recommender systems research of the need to make the recommendation process more transparent to users. Such transparency would lead to a better user satisfaction [10,11]. In a user-centric view, the process only includes two stages, acquisition and communication.…”

Section: A User's Perspective Of Recommendations Deliverymentioning

confidence: 99%

On the Delivery of Recommendations in Social Software: A User’s Perspective

Jiang

Ali

2014

Human-Centered Software Engineering

View full text Add to dashboard Cite

Abstract. Recommendation is a popular feature of social software. Recommendations could be made by the software autonomously or by social contacts who are often aided by the software on what to recommend. A great deal of emphasis in the literature has been given to the algorithmic solution to infer relevant and interesting recommendations. Yet, the delivery method of recommendation is still a widely unexplored research topic. This paper advocates that the success in deducing recommendations is not the sole factor for "recommendees" to consider. Users have their own requirements on the way a recommendation is made and delivered. Failure in meeting user expectations would often lead to the rejection of the recommendations as well as the violation of user experience. In this paper, we conduct an empirical research to explore such user's perspective. We start with qualitative phase, based on interviews, and confirm and enhance the results in a quantitative phase through surveying a large sample of users. We report on the results and conclude with a set of guidelines on how recommendations delivery should be designed from a user's perspective.

show abstract

“…In particular, Gansner et al [25] use geographic maps for TV show recommendations, Kagie et al [26] use two-dimensional maps for product recommendations in ecommerce, and Verbert et al [27] use clustermaps for talk recommendations at scientific conferences. Specifically concentrating on treemaps, Tintarev and Masthoff [16,28] state that this kind of visualization has not been used for recommender systems even though it may be a valuable choice. Based on our literature search, we can verify this claim and confirm that it still holds.…”

Section: Related Work and Research Objectivesmentioning

confidence: 99%

“…Treemaps arrange the recommendations in a way that provides a structured overview of large parts of the search space (i.e. the set of all available alternatives), which facilitates the users' comprehension of the big picture of options fitting their needs [16]. On the one hand, educating the users about the search space helps them understand the reasoning behind the recommendations [17].…”

Section: Introductionmentioning

confidence: 99%

Explorative Analysis of Recommendations Through Interactive Visualization

Richthammer

Pernul

2017

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

Abstract. Even though today's recommender algorithms are highly sophisticated, they can hardly take into account the users' situational needs. An obvious way to address this is to initially inquire the users' momentary preferences, but the users' inability to accurately state them upfront may lead to the loss of several good alternatives. Hence, this paper suggests to generate the recommendations without such additional input data from the users and let them interactively explore the recommended items on their own. To support this explorative analysis, a novel visualization tool based on treemaps is developed. The analysis of the prototype demonstrates that the interactive treemap visualization facilitates the users' comprehension of the big picture of available alternatives and the reasoning behind the recommendations. This helps the users get clear about their situational needs, inspect the most relevant recommendations in detail, and finally arrive at informed decisions.

show abstract

A Survey of Explanations in Recommender Systems

Cited by 410 publications

References 26 publications

An overview of recommender systems in the healthy food domain

An overview of recommender systems in the healthy food domain

On the Delivery of Recommendations in Social Software: A User’s Perspective

Explorative Analysis of Recommendations Through Interactive Visualization

Contact Info

Product

Resources

About