Learning edge importance in bipartite graph-based recommendations

Robert, Kwieciński,; Górecki, Tomasz; Filipowska, Agata

doi:10.15439/2022f191

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…We published an analogous dataset from a different period on Kaggle. 4 We conducted a more comprehensive analysis of this dataset in our previous work [16].…”

Section: Online A/b Testmentioning

confidence: 99%

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Comparison of Real-Time and Batch Job Recommendations

Kwiecinski

Melniczak²,

Górecki

2023

IEEE Access

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Collaborative filtering recommendation systems are traditionally trained in a batch manner and are designed to produce personalized recommendations for a large number of users at the same time. However, in many industrial use cases, it is reasonable to produce recommendations in real time, taking account of very recent user interactions. In this work, we present the implementation of batch and real-time recommendation systems using the example of the RP3Beta model, a simple scalable graph-based model that outperforms multiple more advanced models. Our approach can be utilized by any recommendation system if user-to-item recommendations can be obtained based on item-to-item recommendations. We show that it covers multiple common recommendation models, especially collaborative filtering approaches where user features are not available. We also provide the results of A/B tests comparing these two approaches in a real-world scenario of a job recommendation task, conducted with almost 200,000 OLX users. We report at least 10% more users applying for recommended job ads when using a real-time instead of a batch approach. We believe that our results can help other organizations to take informed decisions about whether to make the effort of moving from a batch to a real-time recommendation setting.INDEX TERMS A/B tests, collaborative filtering, job recommendations, real-time recommendations, RP3Beta.

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“…We published an analogous dataset from a different period on Kaggle. 4 We conducted a more comprehensive analysis of this dataset in our previous work [16].…”

Section: Online A/b Testmentioning

confidence: 99%

“…They usually make use of information about previous interactions between users and items not only when training the models, but also when producing recommendations for users. It has been observed that the most recent user interactions are usually the most important for producing recommendations [4].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Real-Time and Batch Job Recommendations

Kwiecinski

Melniczak²,

Górecki

2023

IEEE Access

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Towards addressing item cold-start problem in collaborative filtering by embedding agglomerative clustering and FP-growth into the recommendation system

Kannout,

Grodzki,

Grzegorowski

2023

COMSIS J

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This paper introduces a frequent pattern mining framework for recommender systems (FPRS) - a novel approach to address the items? cold-start problem. This difficulty occurs when a new item hits the system, and properly handling such a situation is one of the key success factors of any deployment. The article proposes several strategies to combine collaborative and content-based filtering methods with frequent items mining and agglomerative clustering techniques to mitigate the cold-start problem in recommender systems. The experiments evaluated the developed methods against several quality metrics on three benchmark datasets. The conducted study confirmed usefulness of FPRS in providing apt outcomes even for cold items. The presented solution can be integrated with many different approaches and further extended to make up a complete and standalone RS.

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Job Recommendations: Benchmarking of Collaborative Filtering Methods for Classifieds

Kwieciński,

Górecki,

Filipowska

et al. 2024

Electronics

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Classifieds pose numerous challenges for recommendation methods, including the temporary visibility of ads, the anonymity of most users, and the fact that typically only one user can consume an advertised item. In this work, we address these challenges by choosing models and evaluation procedures that are considered accurate, diverse, and efficient (in terms of memory and time consumption during training and prediction). This paper aims to benchmark various recommendation methods for job classifieds, using OLX Jobs as an example, to enhance the conversion rate of advertisements and user satisfaction. In our research, we implement scalable methods and represent different approaches to the recommendations: Alternating Least Square (ALS), LightFM, Prod2Vec, RP3Beta, and Sparse Linear Methods (SLIM). We conducted A/B tests by sending millions of messages with recommendations to perform online evaluations of selected methods. In addition, we have published the dataset created for our research. To the best of our knowledge, this is the first dataset of its kind. It contains 65,502,201 events performed on OLX Jobs by 3,295,942 users who interacted with (displayed, replied to, or bookmarked) 185,395 job ads over two weeks in 2020. We demonstrate that RP3Beta, SLIM, and ALS perform significantly better than Prod2Vec and LightFM when tested in a laboratory setting. Online A/B tests also show that sending messages with recommendations generated by the ALS and RP3Beta models increases the number of users contacting advertisers. Additionally, RP3Beta had a 20% more significant impact on this metric than ALS.

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Learning edge importance in bipartite graph-based recommendations

Cited by 3 publications

References 22 publications

Comparison of Real-Time and Batch Job Recommendations

Comparison of Real-Time and Batch Job Recommendations

Towards addressing item cold-start problem in collaborative filtering by embedding agglomerative clustering and FP-growth into the recommendation system

Job Recommendations: Benchmarking of Collaborative Filtering Methods for Classifieds

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