RecSys Challenge 2016

Pacuk, Andrzej; Sankowski, Piotr; Węgrzycki, Karol; Witkowski, Adam; Wygocki, Piotr

doi:10.1145/2987538.2987544

Cited by 14 publications

(2 citation statements)

References 3 publications

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“…The two datasets include user behaviors from two different application scenarios. The XING dataset is extracted from the Recsys Challenge 2016 dataset [29], which contains a set of user actions on job postings from a professional social network site 4 . Each action is associated with the user ID, item ID, action timestamp and interaction type (click, bookmark, delete, etc.).…”

Section: Datasetmentioning

confidence: 99%

Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation

Cai

Wang

2020

Proceedings of the Web Conference 2020

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Predicting users' preferences based on their sequential behaviors in history is challenging and crucial for modern recommender systems. Most existing sequential recommendation algorithms focus on transitional structure among the sequential actions, but largely ignore the temporal and context information, when modeling the influence of a historical event to current prediction.In this paper, we argue that the influence from the past events on a user's current action should vary over the course of time and under different context. Thus, we propose a Contextualized Temporal Attention Mechanism that learns to weigh historical actions' influence on not only what action it is, but also when and how the action took place. More specifically, to dynamically calibrate the relative input dependence from the self-attention mechanism, we deploy multiple parameterized kernel functions to learn various temporal dynamics, and then use the context information to determine which of these reweighing kernels to follow for each input. In empirical evaluations on two large public recommendation datasets, our model consistently outperformed an extensive set of state-of-the-art sequential recommendation methods.

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Section: Datasetmentioning

confidence: 99%

Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation

Cai

Wang

2020

Proceedings of the Web Conference 2020

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“…Particularly, we utilize the popular XGBoost system (Chen and Guestrin, 2016), a scalable endtoend tree boosting approach which has been shown to be highly suited for recommendation tasks (Pacuk et al, 2016;Ayaki et al, 2017;Tran, 2016). Using XGBoost, we set the learning objective to logistic regression for binary classification, which provides us with the desired probabilities.…”

Section: Recommendation Computationmentioning

confidence: 99%

User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues

Zangerle¹,

Pichl²,

Schedl

2020

Transactions of the International Society for Music Information Retrieval

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Integrating information about the listener's cultural background when building music recommender systems has recently been identified as a means to improve recommendation quality. In this article, we, therefore, propose a novel approach to jointly model users by their musical preferences and cultural backgrounds. We describe the musical preferences of users by the acoustic features of the songs the users have listened to and characterize the cultural background of users by culture-related socioeconomic features that we infer from the user's country. To evaluate the impact of the proposed user model on recommendation quality, we integrate the model into a culture-aware recommender system. By analyzing a dataset comprising approximately 400 million listening events of about 55,000 users from 36 countries, we show that incorporating both acoustic information of the tracks a user has listened to as well as the cultural background of users in the form of a music-cultural user model contributes to improved recommendation performance. Furthermore, we provide a systematic analysis of the influence of different features on the quality of the provided culture-aware track recommendations. We find that considering acoustic features that model the characteristics of tracks and a user's musical preferences have the highest impact on recommendation performance. However, adding socioeconomic features allows further improving the recommendation quality. In addition, we identify interesting correlations between acoustic characteristics of music preferences and cultural features of populations at the country level.

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e-Recruitment recommender systems: a systematic review

Freire

Castro

2020

Knowl Inf Syst

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RecSys Challenge 2016

Cited by 14 publications

References 3 publications

Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation

Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation

User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues

e-Recruitment recommender systems: a systematic review

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