"Double Agents": Assessing the Role of Electronic Product Recommendation Systems

2005 ◽  
Author(s):  
Gerald Häubl ◽  
Kyle B. Murray
Keyword(s):  
Recommendation Systems ◽  
Product Recommendation ◽  
Electronic Product

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

scholarly journals A Review of the Role of Sensors in Mobile Context-Aware Recommendation Systems

2015 ◽  
Vol 11 (11) ◽  
pp. 489264 ◽  
Author(s):  
Sergio Ilarri ◽  
Ramón Hermoso ◽  
Raquel Trillo-Lado ◽  
María del Carmen Rodríguez-Hernández
Keyword(s):  
Recommendation Systems ◽  
Context Aware ◽  
Mobile Context

scholarly journals A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1430
Author(s):  
Guisheng Chen ◽  
Zhanshan Li
Keyword(s):  
State Of The Art ◽  
Recommendation Systems ◽  
Statistical Characteristics ◽  
Periodic Pattern ◽  
Probability Models ◽  
Periodic Patterns ◽  
Market Basket ◽  
Product Recommendation ◽  
Precise Prediction ◽  
Pattern Prediction

Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers’ individual shopping records, where tendency is a new concept to reflect the evolution of customers’ shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.

scholarly journals Queer women’s experiences of patchwork platform governance on Tinder, Instagram, and Vine

2018 ◽  
Vol 26 (2) ◽  
pp. 237-252 ◽  
Author(s):  
Stefanie Duguay ◽  
Jean Burgess ◽  
Nicolas Suzor
Keyword(s):  
Social Media ◽  
New Media ◽  
Recommendation Systems ◽  
Press Coverage ◽  
User Experiences ◽  
Platform Governance ◽  
Governance Practices ◽  
Social Media Platforms ◽  
Cross Platform

Leaked documents, press coverage, and user protests have increasingly drawn attention to social media platforms’ seemingly contradictory governance practices. We investigate the governance approaches of Tinder, Instagram, and Vine through detailed analyses of each platform, using the ‘walkthrough method’ (Light, Burgess, and Duguay, 2016 The walkthrough method: An approach to the study of apps. New Media & Society 20(3).), as well as interviews with their queer female users. Across these three platforms, we identify a common approach we call ‘patchwork platform governance’: one that relies on formal policies and content moderation mechanisms but pays little attention to dominant platform technocultures (including both developer cultures and cultures of use) and their sustaining architectures. Our analysis of these platforms and reported user experiences shows that formal governance measures like Terms of Service and flagging mechanisms did not protect users from harassment, discrimination, and censorship. Key components of the platforms’ architectures, including cross-platform connectivity, hashtag filtering, and algorithmic recommendation systems, reinforced these technocultures. This significantly limited queer women’s ability to participate and be visible on these platforms, as they often self-censored to avoid harassment, reduced the scope of their activities, or left the platform altogether. Based on these findings, we argue that there is a need for platforms to take more systematic approaches to governance that comprehensively consider the role of a platform’s architecture in shaping and sustaining dominant technocultures.

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