FINN.no Slates Dataset: A new Sequential Dataset Logging Interactions, all Viewed Items and Click Responses/No-Click for Recommender Systems Research

Eide, Simen; Leslie, David S.; Frigessi, Arnoldo; Rishaug, Joakim; Jenssen, Helge Kristian; Verrewaere, Sofie

doi:10.1145/3460231.3474607

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Datasets. The evaluation evaluates recommenders on the three existing open-source datasets with impressions, i.e., the Content-Wise Impressions [26], MIND [32], and FINN.no Slates [10,11] datasets. For the ContentWise Impressions dataset all interactions and impressions that produced an interaction are used.…”

Section: Experimental Methodologymentioning

confidence: 99%

“…Week six only does not contains interaction, only impressions data. Lastly, the FINN.no Slates [10,11] dataset contains interactions and impressions of users with the finn.no site; an online service that allows users to sell goods and services. Users are registered accounts on the platform, while items are products listed in the second-hand marketplace.…”

Section: Overview Of Impressions In Recommender Systemsmentioning

confidence: 99%

“…However, this is rapidly changing as more impression datasets have been published and competitions encouraged their use. For instance: (i) interest in impressions research and use has risen [6,22], (ii) several RS challenges included impressions in their datasets, (iii) industries have presented case studies highlighting the effects of impressions data in their recommendations [4,19], and (iv) three open datasets were recently-published ContentWise Impressions [26], MIND [32], and FINN.no Slates [10,11]. Due to the limited use of impressions in RS, there exist several open research questions, e.g., whether to evaluate recommendation models with impressions data requires to develop a specific methodology as is the case in other scenarios [12,14], characterization of signals and biases in impressions, and challenges regarding the use of impressions.…”

Section: Introductionmentioning

confidence: 99%

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Towards the Evaluation of Recommender Systems with Impressions

Maurera

Dacrema

Cremonesi

2022

Proceedings of the 16th ACM Conference on Recommender Systems

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In Recommender Systems, impressions are a relatively new type of information that records all products previously shown to the users. They are also a complex source of information, combining the effects of the recommender system that generated them, search results, or business rules that may select specific products for recommendations. The fact that the user interacted with a specific item given a list of recommended ones may benefit from a richer interaction signal, in which some items the user did not interact with may be considered negative interactions. This work presents a preliminary evaluation of recommendation models with impressions. First, impressions are characterized by describing their assumptions, signals, and challenges. Then, an evaluation study with impressions is described. The study's goal is two-fold: to measure the effects of impressions data on properly-tuned recommendation models using current open-source datasets and disentangle the signals within impressions data. Preliminary results suggest that impressions data and signals are nuanced, complex, and effective at improving the recommendation quality of recommenders. This work publishes the source code, datasets, and scripts used in the evaluation to promote reproducibility in the domain.

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Section: Experimental Methodologymentioning

confidence: 99%

Section: Overview Of Impressions In Recommender Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards the Evaluation of Recommender Systems with Impressions

Maurera

Dacrema

Cremonesi

2022

Proceedings of the 16th ACM Conference on Recommender Systems

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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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Enhancing Counterfactual Evaluation and Learning for Recommendation Systems

Felicioni

2022

Proceedings of the 16th ACM Conference on Recommender Systems

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FINN.no Slates Dataset: A new Sequential Dataset Logging Interactions, all Viewed Items and Click Responses/No-Click for Recommender Systems Research

Cited by 5 publications

References 6 publications

Towards the Evaluation of Recommender Systems with Impressions

Towards the Evaluation of Recommender Systems with Impressions

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

Enhancing Counterfactual Evaluation and Learning for Recommendation Systems

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