The Pure Cold-Start Problem: A deep study about how to conquer first-time users in recommendations domains. (February 2019)
- Record Type:
- Journal Article
- Title:
- The Pure Cold-Start Problem: A deep study about how to conquer first-time users in recommendations domains. (February 2019)
- Main Title:
- The Pure Cold-Start Problem: A deep study about how to conquer first-time users in recommendations domains
- Authors:
- Silva, Nícollas
Carvalho, Diego
Pereira, Adriano C.M.
Mourão, Fernando
Rocha, Leonardo - Abstract:
- Abstract: The success of Web-based applications depends on their ability to convert first-time users into recurring ones. This problem is known as Pure Cold-Start and it refers to the capability of Recommender Systems (RSs) providing useful recommendations to users without historical data. Traditionally, the systems assume that items biased by popularity, recency and positive ratings suit the interests of most first-time users. However, our studies confirm a contra-intuitive hypothesis, showing users consumption preferences biased to non-popular items. For this reason, we introduce two new RSs to mitigate this problem based on user coverage maximization: Max-Coverage and Category-Exploration. Offline experiments show that our recommendations complement the traditional ones. Thus, we found an opportunity for improvements on state-of-the-art RSs. We hypothesize that combining distinct non-personalized RSs can be better to conquer the most first-time users than traditional ones. An online study conducted with 236 real users in movie domain reinforced this hypothesis. Hence, this study provides a clear message: we should compose product pages that mix complementary non-personalized RSs. Highlights: Use of complementary non-personalized RSs to conquer most first-time users. The evaluation of new research hypotheses related to the Pure Cold Start. The proposal of two novel solutions to mitigate the Pure Cold Start problem. Discussion about some practical implications of this studyAbstract: The success of Web-based applications depends on their ability to convert first-time users into recurring ones. This problem is known as Pure Cold-Start and it refers to the capability of Recommender Systems (RSs) providing useful recommendations to users without historical data. Traditionally, the systems assume that items biased by popularity, recency and positive ratings suit the interests of most first-time users. However, our studies confirm a contra-intuitive hypothesis, showing users consumption preferences biased to non-popular items. For this reason, we introduce two new RSs to mitigate this problem based on user coverage maximization: Max-Coverage and Category-Exploration. Offline experiments show that our recommendations complement the traditional ones. Thus, we found an opportunity for improvements on state-of-the-art RSs. We hypothesize that combining distinct non-personalized RSs can be better to conquer the most first-time users than traditional ones. An online study conducted with 236 real users in movie domain reinforced this hypothesis. Hence, this study provides a clear message: we should compose product pages that mix complementary non-personalized RSs. Highlights: Use of complementary non-personalized RSs to conquer most first-time users. The evaluation of new research hypotheses related to the Pure Cold Start. The proposal of two novel solutions to mitigate the Pure Cold Start problem. Discussion about some practical implications of this study in real scenarios. … (more)
- Is Part Of:
- Information systems. Volume 80(2019)
- Journal:
- Information systems
- Issue:
- Volume 80(2019)
- Issue Display:
- Volume 80, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 80
- Issue:
- 2019
- Issue Sort Value:
- 2019-0080-2019-0000
- Page Start:
- 1
- Page End:
- 12
- Publication Date:
- 2019-02
- Subjects:
- Non-personalized recommender systems -- Pure Cold-Start problem -- First-time users -- e-commerce systems
Database management -- Periodicals
Electronic data processing -- Periodicals
Bases de données -- Gestion -- Périodiques
Informatique -- Périodiques
Database management
Electronic data processing
Periodicals
005.7 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03064379 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.is.2018.09.001 ↗
- Languages:
- English
- ISSNs:
- 0306-4379
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4496.367300
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9005.xml