From the lab to production: A case study of session-based recommendations in the home-improvement domain

One of the most important concerns about recommender systems is the filter bubble phenomenon. While recommender systems try to personalize information, they tighten the filter bubble around the users and deprive them of a wide range of content. To overcome this problem, one can diversify the personalized recommendation list. A diversified list usually presents a broader content to the user. Session-based recommender systems are types of recommenders in which only the current session of the user is available, and therefore, they should recommend the next item given the items in the current session. While diversifying conventional recommender systems has been well assessed in the literature, it has gained less attention in session-based recommenders. Diversity and accuracy usually have a negative correlation, i.e., by improving one the other one will be declined. In this study, we propose diversity and accuracy enhancing approaches based on sequential rule mining and session-based k-nearest neighbor methods. Finally, we propose a performance balancing approach that improves both the diversity and accuracy of these session-based recommender systems. We demonstrate the performance of the proposed methods on four music recommender datasets. Keywords Session-based recommenders • Diversity • Session-based k-nearest neighbor • Sequential rule mining • Filter bubble phenomenonThis article is part of the topical collection "Advanced Theories and Algorithms for Next-generation Recommender Systems" guest edited by

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“…accuracy measures compared to more complex methods. These results are also supported by other studies [22][23][24][25][26][27][28].…”

Section: Session-based Recommender Systemssupporting

confidence: 91%

Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems

Gharahighehi

Vens

2021

SN COMPUT. SCI.

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“…Regarding the RS evaluation methods that are used in our work, it is important to cite other studies that have touched some of the issues discussed in this paper. In Kouki et al (2020) the authors note that off-line evaluation does not provide enough information to identify the best RS, for all the considered metrics. They also note that the precision metric scores high an algorithm only when it predicts the exact same item that the user choose.…”

Section: Related Workmentioning

confidence: 99%

“…This metric is scoring high the items that have the properties of the items, which are typically chosen by a user in a context, but they might not be the same that were actually selected. In Kouki et al (2020) the authors also conduct a user study, but while in their case the recommendations were evaluated by experts, on behalf of the real users, we directly asked the subjects who received the recommendations to evaluate them. Moreover, we must observe that the application domain is very different, as they focused on the home-improvement domain.…”

Section: Related Workmentioning

confidence: 99%

Popularity, novelty and relevance in point of interest recommendation: an experimental analysis

Massimo

Ricci

2021

Inf Technol Tourism

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Recommender Systems (RSs) are often assessed in off-line settings by measuring the system precision in predicting the observed user’s ratings or choices. But, when a precise RS is on-line, the generated recommendations can be perceived as marginally useful because lacking novelty. The underlying problem is that it is hard to build an RS that can correctly generalise, from the analysis of user’s observed behaviour, and can identify the essential characteristics of novel and yet relevant recommendations. In this paper we address the above mentioned issue by considering four RSs that try to excel on different target criteria: precision, relevance and novelty. Two state of the art RSs called and follow a classical Nearest Neighbour approach, while the other two, and are based on Inverse Reinforcement Learning. and optimise precision, tries to identify the characteristics of POIs that make them relevant, and , a novel RS here introduced, is similar to but it also tries to recommend popular POIs. In an off-line experiment we discover that the recommendations produced by and optimise precision essentially by recommending quite popular POIs. can be tuned to achieve a desired level of precision at the cost of losing part of the best capability of to generate novel and yet relevant recommendations. In the on-line study we discover that the recommendations of and are liked more than those produced by . The rationale of that was found in the large percentage of novel recommendations produced by , which are difficult to appreciate. However, excels in recommending items that are both novel and liked by the users.

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“…For example: sessions are split using "a commonly used 30-minute user inactivity threshold" instead of analyzing user behaviors to select the inactivity threshold. Lastly, [9], [23]- [25] present evaluation studies similar to this work, but they are performed in a different context or recommendation scenario.…”

Section: Related Workmentioning

confidence: 99%

“…These constructions steps have a strong impact, equal to or even greater than that of the algorithm itself, on the final quality of the recommendation process [7], [8]. However, few works using industrial datasets [6], [9] have addressed these important aspects. In this work, the construction, processing, and cleaning of an industrial dataset is described in detail.…”

Section: Introductionmentioning

confidence: 99%

From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain

et al. 2022

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This work presents a comprehensive study, from an industrial perspective, of the process between the collection of raw data, and the generation of next-item recommendation, in the domain of Video-on-Demand (VoD). Most research papers focus their efforts on analyzing recommender systems on already-processed datasets, but they do not face the same challenges that occur naturally in industry, e.g., processing raw interactions logs to create datasets for testing. This paper describes the whole process between data collection and recommendation, including cleaning, processing, feature engineering, session inferring, and all the challenges that a dataset provided by an industrial player in the domain posed. Then, a comparison on the new dataset of several intent-based recommendation techniques in the nextitem recommendation task follows, studying the impact of different factors like the session length, and the number of previous sessions available for a user. The results show that taking advantage of the sequential data available in the dataset benefits recommendation quality, since deep learning algorithms for sessionaware recommendation are consistently the most accurate recommenders. Lastly, a summary of the different challenges in the VoD domain is proposed, discussing on the best algorithmic solutions found, and proposing future research directions to be conducted based on the results obtained.

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From the lab to production: A case study of session-based recommendations in the home-improvement domain

Cited by 21 publications

References 21 publications

Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems

Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems

Popularity, novelty and relevance in point of interest recommendation: an experimental analysis

From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain

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