The Continuous Cold Start Problem in e-Commerce Recommender Systems

Bernardi, Lucas; Kamps, Jaap; Kiseleva, Julia; Müller, Melanie JI

doi:10.48550/arxiv.1508.01177

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…Multi-armed bandits are a classical exploration-exploitation framework from Reinforcement Learning (RL), where the user feedback is available in each iteration [24,56,71]. They are becoming popular for online applications such as adds ranking and recommendation systems [e.g., 9,44], where information about user preferences is unavailable (cold-start users [12,48]) [32]. Parapar and Radlinski [71] proposed a multi-armed bandit model for personalized recommendations by diversifying the user preferences.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The task becomes especially challenging since most real applications [18] need to constantly deal with cold-start users [48,80], for whom little to no preferential knowledge is known a priori. This may be due to infrequent visits, rapid changes in user preferences [12,46,47], or general privacy-preserving constraints. In this work, we aim to bridge the described gap of processing complex information-seeking requests in natural language from unknown users by developing a new type of application, which will work as illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Supporting Complex Information-Seeking Tasks with Implicit Constraints

Ahmadvand¹,

Arabzadeh²,

Kiseleva³

et al. 2022

Preprint

Self Cite

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Current interactive systems with natural language interface lack an ability to understand a complex information-seeking request which expresses several implicit constraints at once, and there is no prior information about user preferences, e.g., 'find hiking trails around San Francisco which are accessible with toddlers and have beautiful scenery in summer', where output is a list of possible suggestions for users to start their exploration. In such scenarios, the user requests can be issued at once in the form of a complex and long query, unlike conversational and exploratory search models that require short utterances or queries where they often require to be fed into the system step by step. This advancement provides the final user more flexibility and precision in expressing their intent through the search process. Such systems are inherently helpful for day-today user tasks requiring planning that are usually time-consuming, sometimes tricky, and cognitively taxing. We have designed and deployed a platform to collect the data from approaching such complex interactive systems.In this paper, we propose an Interactive Agent (IA) that allows intricately refined user requests by making it complete, which should lead to better retrieval. To demonstrate the performance of the proposed modeling paradigm, we have adopted various pre-retrieval metrics that capture the extent to which guided interactions with our system yield better retrieval results. Through extensive experimentation, we demonstrated that our method significantly outperforms several robust baselines.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supporting Complex Information-Seeking Tasks with Implicit Constraints

Ahmadvand¹,

Arabzadeh²,

Kiseleva³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Existing approaches fail to provide cold start recommendations while maintaining the important needs of such communities. Content-based cold start approaches [2,18] rely on user content information, which is invasive on user personal data, and does not work effectively in the setting of new users, for whom no preferences are given. Likewise, collaborative filtering [5,12,23], which provides recommendations based on preference-based similarity to other users or items, struggles with cold start users and items.…”

Section: Introductionmentioning

confidence: 99%

Privacy preserving cold-start recommendation for out-of-matrix users via content baskets

Sun

Wang

2023

Int J Data Sci Anal

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Guest users, single-time clients who use an online service anonymously without prior registration, are common in real world recommendation applications, requiring industrial recommendation systems to handle the "cold start" problem in which no existing interactions between new users and recommendable items can be drawn from to make predictions.Prior work addresses this problem by learning profiling user representations to bootstrap recommendations for new users. However, this process can often be invasive, requiring new users to submit personal data, or shallow, yielding unexpressive representations for accurate recommendations.In this work, we propose new representations for guest users based on their "content basket". A set of seed items is submitted by the user to use the service, allowing each user to be represented as a function of a collection of items. Simultaneously, we design a graph representation space in which items (nodes) are connected by edges that signify joint, written recommendations between items.We propose a graph neural network architecture that inductively learns item and inter-item (edge) representations as a combination of deep language encodings of textual content descriptions and graph embeddings learned via message passing on the edges. This scheme enables effective generalization to items unseen during training. To demonstrate the effectiveness of our model on a real-world setting in which guest users are prevalent, we present a new dataset for anime recommendations, AnimeULike, containing anonymized interactions between 13k users and 10k animes, with an accompanying recommendation engine which can exclusively serve guest users.Our empirical results on AnimeULike and a standard recommender systems benchmark dataset demonstrate significant performance improvements over previous cold start solutions that do not learn to dynamically represent new users.

show abstract

Section: Introductionmentioning

confidence: 99%

Graph Inductive Inference of Personalized Content for Out-of-matrix Users Via Content Baskets

Sun

Wang

2022

Preprint

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Guest users are common in real world applications, requiring industrial recommendation systems to handle the "cold start" problem, where no existing interactions between new users and recommendable items can be drawn from to make predictions. Prior work addresses this problem by learning profiling user representations to bootstrap recommendations for new users. However, this process can often be invasive, requiring new users to submit personal data, or shallow, yielding unexpressive representations for accurate recommendations. In this work, we propose new representations for guest users based on their ``content basket’'. A set of seed items is submitted by the user to use the service, allowing each user to be represented as a function of a collection of items. Simultaneously, we design a graph representation space, where items (nodes) are connected by edges that signify joint, written recommendations between items. We propose a graph neural network architecture that inductively learns item and inter-item (edge) representations as a combination of deep language encodings of textual content descriptions and graph embeddings learned via message passing on the edges. This enables effective generalization to items unseen during training. To evaluate our model and demonstrate a novel application, we present a new dataset for anime recommendations, AnimeULike, containing anonymized interactions between ~13k users and 10k animes, and the accompanying recommendation engine which can exclusively serve guest users. Our empirical results on AnimeULike and a standard recommender systems benchmark demonstrate significant performance improvements over previous cold start solutions that do not learn to dynamically represent new users.

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The Continuous Cold Start Problem in e-Commerce Recommender Systems

Cited by 3 publications

References 10 publications

Supporting Complex Information-Seeking Tasks with Implicit Constraints

Supporting Complex Information-Seeking Tasks with Implicit Constraints

Privacy preserving cold-start recommendation for out-of-matrix users via content baskets

Graph Inductive Inference of Personalized Content for Out-of-matrix Users Via Content Baskets

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