Workshop on user-centric evaluation of recommender systems and their interfaces

2010 ◽  
Author(s):  
Bart P. Knijnenburg ◽  
Lars Schmidt-Thieme ◽  
Dirk G.F.M. Bollen
Keyword(s):  
Recommender Systems ◽  
User Centric

scholarly journals Symmetric Metric Learning with Adaptive Margin for Recommendation

2020 ◽  
Vol 34 (04) ◽  
pp. 4634-4641
Author(s):  
Mingming Li ◽  
Shuai Zhang ◽  
Fuqing Zhu ◽  
Wanhui Qian ◽  
Liangjun Zang ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Metric Space ◽  
State Of The Art ◽  
Metric Learning ◽  
Current User ◽  
Experimental Results ◽  
Negative Item ◽  
Positive Item ◽  
User Centric ◽  
The Impact

Metric learning based methods have attracted extensive interests in recommender systems. Current methods take the user-centric way in metric space to ensure the distance between user and negative item to be larger than that between the current user and positive item by a fixed margin. While they ignore the relations among positive item and negative item. As a result, these two items might be positioned closely, leading to incorrect results. Meanwhile, different users usually have different preferences, the fixed margin used in those methods can not be adaptive to various user biases, and thus decreases the performance as well. To address these two problems, a novel Symmetic Metric Learning with adaptive margin (SML) is proposed. In addition to the current user-centric metric, it symmetically introduces a positive item-centric metric which maintains closer distance from positive items to user, and push the negative items away from the positive items at the same time. Moreover, the dynamically adaptive margins are well trained to mitigate the impact of bias. Experimental results on three public recommendation datasets demonstrate that SML produces a competitive performance compared with several state-of-the-art methods.

Item Familiarity as a Possible Confounding Factor in User-Centric Recommender Systems Evaluation

i-com ◽  
2015 ◽  
Vol 14 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Dietmar Jannach ◽  
Lukas Lerche ◽  
Michael Jugovac
Keyword(s):  
Recommender Systems ◽  
User Satisfaction ◽  
User Study ◽  
Predictive Accuracy ◽  
Confounding Factor ◽  
Academic Research ◽  
Strongly Correlated ◽  
Quality Factors ◽  
User Centric ◽  
Assess Quality

AbstractUser studies play an important role in academic research in the field of recommender systems as they allow us to assess quality factors other than the predictive accuracy of the underlying algorithms. User satisfaction is one such factor that is often evaluated in laboratory settings and in many experimental designs one task of the participants is to assess the suitability of the system-generated recommendations. The effort required by the user to make such an assessment can, however, depend on the user’s familiarity with the presented items and directly impact on the reported user satisfaction. In this paper, we report the results of a preliminary recommender systems user study using Mechanical Turk, which indicates that item familiarity is strongly correlated with overall satisfaction.

scholarly journals A User-Centric Housing Recommender System

2018 ◽  
Vol 10 (3) ◽  
pp. 17-24
Author(s):  
Ojokoh B.A. ◽  
Olayemi O.C. ◽  
Babalola A.E. ◽  
Eyo E.O.
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Choice Making ◽  
Decision Making Processes ◽  
User Centric ◽  
Cost Implications ◽  
House Buyers

 Recommender systems are very useful in assisting users to reduce the complexities involved in their decision making processes. It is particularly difficult for people to make decisions on housing choices because different options exist with different facilities, in different locations and with varied cost implications. This paper proposes a hybrid user-centric housing recommender system that is implemented to assist potential house buyers and tenants to generate house listings based on their preferences with the aid of fuzzy logic and item-based collaborative filtering. A virtual tour of the houses is also provided for better choice making.   

A Characterisation and Framework for User-Centric Factors in Evaluation Methods for Recommender Systems

2017 ◽  
Vol 6 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Tatenda D. Kavu ◽  
Kuda Dube ◽  
Peter G. Raeth ◽  
Gilford T. Hapanyengwi
Keyword(s):  
Conceptual Framework ◽  
Recommender Systems ◽  
User Experience ◽  
User Satisfaction ◽  
Optimal Solution ◽  
User Interaction ◽  
Evaluation Methods ◽  
User Preferences ◽  
Evaluation Framework ◽  
User Centric

Researchers have worked on-finding e-commerce recommender systems evaluation methods that contribute to an optimal solution. However, existing evaluations methods lack the assessment of user-centric factors such as buying decisions, user experience and user interactions resulting in less than optimum recommender systems. This paper investigates the problem of adequacy of recommender systems evaluation methods in relation to user-centric factors. Published work has revealed limitations of existing evaluation methods in terms of evaluating user satisfaction. This paper characterizes user-centric evaluation factors and then propose a user-centric evaluation conceptual framework to identify and expose a gap within literature. The researchers used an integrative review approach to formulate both the characterization and the conceptual framework for investigation. The results reveal a need to come up with a holistic evaluation framework that combines system-centric and user-centric evaluation methods as well as formulating computational user-centric evaluation methods. The conclusion reached is that, evaluation methods for e-commerce recommender systems lack full assessment of vital factors such as: user interaction, user experience and purchase decisions. A full consideration of these factors during evaluation will give birth to new types of recommender systems that predict user preferences using user decision-making process profiles, and that will enhance user experience and increase revenue in the long run.

scholarly journals User-Centric Evaluation of Recommender Systems in Social Learning Platforms: Accuracy is Just the Tip of the Iceberg

2018 ◽  
Vol 11 (3) ◽  
pp. 294-306 ◽  
Author(s):  
Soude Fazeli ◽  
Hendrik Drachsler ◽  
Marlies Bitter-Rijpkema ◽  
Francis Brouns ◽  
Wim van der Vegt Brouns ◽  
...  
Keyword(s):  
Social Learning ◽  
Recommender Systems ◽  
Learning Platforms ◽  
User Centric
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