Recommender systems based on quantitative implicit customer feedback

Bauer, J.G.; Νανόπουλος, Αλέξανδρος

doi:10.1016/j.dss.2014.09.005

Cited by 39 publications

(21 citation statements)

References 5 publications

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“…Implicit feedback is also important in understanding users' preferences, which are inferred indirectly through observation of user behavior. Although this method does not require the same effort from the users, it is often seen as less accurate [57,66]. Hybrid feedback is considered a combination of explicit and implicit feedback.…”

Section: Information Collection Phasementioning

confidence: 99%

Fashion Recommendation Systems, Models and Methods: A Review

et al. 2021

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In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems.

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Section: Information Collection Phasementioning

confidence: 99%

Fashion Recommendation Systems, Models and Methods: A Review

et al. 2021

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“…Since the above approaches rely on explicit user feedbacks, recommendation is not possible when user-ratings are unavailable. In comparison, some recommendation methods, in particular for web personalization, use 'implicit' click log data or search history data instead of 'explicit' user-ratings [4,7,12,14,19,32,36,42,49,50,54,57]. These methods infer the user's preference from the items clicked by the user.…”

Section: Related Workmentioning

confidence: 99%

A tripartite-graph based recommendation framework for price-comparison services

et al. 2019

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The recommender systems help users who are going through numerous items (e.g., movies or music) presented in online shops by capturing each user?s preferences on items and suggesting a set of personalized items that s/he is likely to prefer [8]. They have been extensively studied in the academic society and widely utilized in many online shops [33]. However, to the best of our knowledge, recommending items to users in price-comparison services has not been studied extensively yet, which could attract a great deal of attention from shoppers these days due to its capability to save users? time who want to purchase items with the lowest price [31]. In this paper, we examine why existing recommendation methods cannot be directly applied to price-comparison services, and propose three recommendation strategies that are tailored to price-comparison services: (1) using click-log data to identify users? preferences, (2) grouping similar items together as a user?s area of interest, and (3) exploiting the category hierarchy and keyword information of items. We implement these strategies into a unified recommendation framework based on a tripartite graph. Through our extensive experiments using real-world data obtained from Naver shopping, one of the largest price-comparison services in Korea, the proposed framework improved recommendation accuracy up to 87% in terms of precision and 129% in terms of recall, compared to the most competitive baseline.

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“…• Implicit feedback (Bauer and Nanopoulos, 2014;Yao et al, 2015): users' preferences are inferred from their navigation trails and interactions with the available content. Hereby, several indicators may be employed in order to transform users' logs into interests such as items consultations' duration, clicks and queries.…”

Section: Data Acquisition and Modellingmentioning

confidence: 99%

An adaptive and interactive recommendation model for consumers' behaviours prediction

Haddad

Baazaoui

2018

IJDMMM

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Recommendation algorithms aim at predicting customers' interests and purchases using different ideas and hypotheses. Consequently, system designers need to choose the recommendation approach that is the most suitable with regard to their products' nature and consumers' behaviours within the application field. In this paper, we propose an adaptive recommendation model based on statistical modelling to assist consumers facing choice overload by predicting their interests and consumption behaviours. We also propose a dynamic variant of the model taking into account the recommendations' time-value during interactive online recommendation scenarios. Our proposal has endured a two-fold evaluation. First, we conducted an offline comparative study on the MovieLens recommendation dataset in order to assess our model's performance with regard to several widely adopted recommendation techniques. Then, the model was evaluated within a real time online news recommendation platform to highlight its adaptability, scalability and efficiency in a highly interactive application domain.

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Recommender systems based on quantitative implicit customer feedback

Cited by 39 publications

References 5 publications

Fashion Recommendation Systems, Models and Methods: A Review

Fashion Recommendation Systems, Models and Methods: A Review

A tripartite-graph based recommendation framework for price-comparison services

An adaptive and interactive recommendation model for consumers' behaviours prediction

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