scholarly journals The performance of recommender systems in online shopping: A user-centric study

2013 ◽  
Vol 40 (14) ◽  
pp. 5551-5562 ◽  
Author(s):  
Maciej Dabrowski ◽  
Thomas Acton
Keyword(s):  
Recommender Systems ◽  
Online Shopping ◽  
User Centric

Semantic Web mining for Content-Based Online Shopping Recommender Systems

2019 ◽  
Vol 15 (4) ◽  
pp. 41-56 ◽  
Author(s):  
Ibukun Tolulope Afolabi ◽  
Opeyemi Samuel Makinde ◽  
Olufunke Oyejoke Oladipupo
Keyword(s):  
Semantic Web ◽  
Recommender Systems ◽  
Web Mining ◽  
Online Shopping ◽  
Semantic Analysis ◽  
Second Phase ◽  
Web Content ◽  
Textual Data ◽  
Content Mining ◽  
Bayes Algorithm

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

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 Various Recommender Systems and Their usage in the Domains of Mobile Based and Online Applications

2019 ◽  
Vol 8 (11S2) ◽  
pp. 170-172
Keyword(s):  
Recommender Systems ◽  
Online Shopping ◽  
Past History ◽  
User Profile ◽  
Key Factors ◽  
User Query ◽  
Current Product ◽  
Internal Mechanism ◽  
Hybrid Recommender ◽  
Intelligent Assistance

The activities of the users are surrounded by online shopping, online content fetching and online payment of the various bills. The important point is in case of the online shopping with Amazon, Flipkart and many other online shopping sites provides some sort of intelligent assistance to the user. Based on the past history, based on the user profile. Such kind of the applications were named as recommender systems. The most common categories of recommender systems involves, the collaborative filtering, content-based filtering, multi-criteria recommender systems, risk-aware recommender systems, mobile recommender systems and Hybrid recommender systems. The current work dealing with the process followed by the recommender systems along with various key factors embedded in the usage. The suggestions given by the application to the user depends on user profile, and content searched by the user and the collaboration of other products with the current product. The work focus on the implementation algorithms existing in the process of recommender systems, the above listed categories of recommender systems follow certain key mechanisms depending on the user query. The work also deals with the performance aspects of the recommender systems in case of accuracy and reproducibility in recommender system research. Especially the mobile recommender systems there are certain limitations of region and accuracy of the results. Overall the outcome of the work is to describe the importance of the recommender systems and the internal mechanism followed by various recommender systems.

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