Two Decades of Recommender Systems at Amazon.com

Smith, Brent; Linden, Greg

doi:10.1109/mic.2017.72

Cited by 475 publications

(252 citation statements)

References 8 publications

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“…First, by representing a user with her consumed items, ICF encodes more signal in its input than UCF that simply uses an ID to represent a user. This provides ICF more potential to improve both the accuracy [9] and interpretability [36] of user preference modeling. For example, there are several empirical evidences on accuracy superiority of ICF over UCF methods for top-N recommendation [8,9,42]; and ICF can interpret a recommended item as its high similarity with some items that the user has consumed before, which would be more acceptable by users than "similar users" based explanation scheme [46].…”

Section: Why Item-based Collaborative Filtering?mentioning

confidence: 99%

“…The matrix factorization (MF) model [17] is a representative user-based CF method (short for UCF), which represents a user with an ID and projects the ID into the same embedding space of items; then the relevance score between a user-item pair is estimated as the inner product of the user embedding and item embedding. In contrast, item-based CF (short for ICF) represents a user with her historically interacted items, using the similarity between the target item and interacted items to estimate the user-item relevance [15,36].…”

Section: Introductionmentioning

confidence: 99%

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Deep Item-based Collaborative Filtering for Top-N Recommendation

Xue

Wang

et al. 2019

ACM Trans. Inf. Syst.

266

126

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Item-based Collaborative Filtering (short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users.In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g., similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.

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Section: Why Item-based Collaborative Filtering?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Item-based Collaborative Filtering for Top-N Recommendation

Xue

Wang

et al. 2019

ACM Trans. Inf. Syst.

266

126

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“…In 2010, YouTube reported using Amazon's algorithm for recommending videos (Davidson, Liebald & Liu 2010). In 2015, 30 percent of Amazon.com's page views were from recommendations (Sharma, Hofman & Watts 2015;Smith & Linden 2017).…”

Section: The Impact Of Recommender Systems On Cultural Marketmentioning

confidence: 99%

Recommender systems as “tastemakers”: Collaborative filtering as a market strategy for online cultural products

Santini

2018

(OBS*)

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In the cultural market, the operation of which depends on the creation of intersubjective values to generate the production of consumers, the market strategies to adjust supply and demand have been historically characterised by mechanisms of social recommendation; a phenomenon that has intensified and changed on the Internet. The aim of this paper is to discuss the social conditions for the production of belief in symbolic goods and their applications in online markets. Pierre Bourdieu's theory on the field of cultural production provides the conceptual grounding for the proposal of a theoretical study that compares traditional instances of symbolic goods recommendation and today's online recommender systems. This analysis diagnoses the transformation of social processes of influence on online cultural consumption through collaborative filtering and identifies relevant topics for future research in the domain of cultural goods e-commerce.

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“…It not only can facilitate the information-seeking process of users, but also can increase the traffic and bring profits to the service provider [1]. Among the various recommendation methods, item-based collaborative filtering (ICF) stands out owing to its interpretability and effectiveness [16,20], being highly preferred in industrial applications [8,10,32]. The key assumption of ICF is that a user shall prefer the items that are similar to her historically interacted items [31,39,45].…”

Section: Introductionmentioning

confidence: 99%

Relational Collaborative Filtering

Xin

Zhang

et al. 2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

143

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Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity -i.e., the item similarity evidenced by user interactions like ratings and purchases. Nevertheless, there exist multiple relations between items in real-world scenarios, e.g., two movies share the same director, two products complement with each other, etc. Distinct from the collaborative similarity that implies co-interact patterns from the user's perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research.In this work, we propose Relational Collaborative Filtering (RCF) to exploit multiple item relations in recommender systems. We find that both the relation type (e.g., shared director) and the relation value (e.g., Steven Spielberg) are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference -the first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with user preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF 1 . Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by RCF's modeling of multiple item relations. CCS CONCEPTS• Information systems → Recommender systems; Retrieval models and ranking; Novelty in information retrieval.

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Two Decades of Recommender Systems at Amazon.com

Cited by 475 publications

References 8 publications

Deep Item-based Collaborative Filtering for Top-N Recommendation

Deep Item-based Collaborative Filtering for Top-N Recommendation

Recommender systems as “tastemakers”: Collaborative filtering as a market strategy for online cultural products

Relational Collaborative Filtering

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