A Collective Variational Autoencoder for Top-NN Recommendation with Side Information

Recommender systems have been studied extensively due to their practical use in many real-world scenarios. Despite this, generating effective recommendations with sparse user ratings remains a challenge. Side information associated with items has been widely utilized to address rating sparsity. Existing recommendation models that use side information are linear and, hence, have restricted expressiveness. Deep learning has been used to capture non-linearities by learning deep item representations from side information but as side information is high-dimensional existing deep models tend to have large input dimensionality, which dominates their overall size. This makes them difficult to train, especially with small numbers of inputs. Rather than learning item representations, which is problematic with high-dimensional side information, in this paper, we propose to learn feature representation through deep learning from side information. Learning feature representations, on the other hand, ensures a sufficient number of inputs to train a deep network. To achieve this, we propose to simultaneously recover user ratings and side information, by using a Variational Autoencoder (VAE). Specifically, user ratings and side information are encoded and decoded collectively through the same inference network and generation network. This is possible as both user ratings and side information are data associated with items. To account for the heterogeneity of user rating and side information, the final layer of the generation network follows different distributions depending on the type of information. The proposed model is easy to implement and efficient to optimize and is shown to outperform state-of-the-art top-$N$ recommendation methods that use side information.Comment: 7 pages, 3 figures, DLRS workshop 201

Embarrassingly Shallow Autoencoders for Sparse Data

Combining simple elements from the literature, we define a linear model that is geared toward sparse data, in particular implicit feedback data for recommender systems. We show that its training objective has a closed-form solution, and discuss the resulting conceptual insights. Surprisingly, this simple model achieves better ranking accuracy than various state-of-the-art collaborative-filtering approaches, including deep non-linear models, on most of the publicly available data-sets used in our experiments.Comment: In the proceedings of the Web Conference (WWW) 2019 (7 pages

Estimate features relevance for groups of users

In item cold-start, collaborative filtering techniques cannot be used directly since newly added items have no interactions with users. Hence, content-based filtering is usually the only viable option left. In this paper we propose a feature-based machine learning model that addresses the item cold-start problem by jointly exploiting item content features, past user preferences and interactions of similar users. The pro- posed solution learns a relevance of each content feature referring to a community of similar users. In our experiments, the proposed approach outperforms classical content-based filtering on an enriched version of the Netflix datase

Style Conditioned Recommendations

We propose Style Conditioned Recommendations (SCR) and introduce style injection as a method to diversify recommendations. We use Conditional Variational Autoencoder (CVAE) architecture, where both the encoder and decoder are conditioned on a user profile learned from item content data. This allows us to apply style transfer methodologies to the task of recommendations, which we refer to as injection. To enable style injection, user profiles are learned to be interpretable such that they express users' propensities for specific predefined styles. These are learned via label-propagation from a dataset of item content, with limited labeled points. To perform injection, the condition on the encoder is learned while the condition on the decoder is selected per explicit feedback. Explicit feedback can be taken either from a user's response to a style or interest quiz, or from item ratings. In the absence of explicit feedback, the condition at the encoder is applied to the decoder. We show a 12% improvement on NDCG@20 over the traditional VAE based approach and an average 22% improvement on AUC across all classes for predicting user style profiles against our best performing baseline. After injecting styles we compare the user style profile to the style of the recommendations and show that injected styles have an average +133% increase in presence. Our results show that style injection is a powerful method to diversify recommendations while maintaining personal relevance. Our main contribution is an application of a semi-supervised approach that extends item labels to interpretable user profiles.Comment: 9 pages, 10 figures, Accepted to RecSys '1

콜드 스타트 비디오 추천시스템을 위한 컨텐츠 표현 학습

학위논문(석사) -- 서울대학교대학원 : 데이터사이언스대학원 데이터사이언스학과, 2023. 2. 이준석.Cold-start item recommendation is a long-standing challenge in recommendation systems. A common approach to tackle cold-start problem is using content-based approach, but in movie recommendations, rich information available in raw video contents or textual descriptions has not been fully utilized. In this paper, we propose a general cold-start recommendation framework that learns multimodal content representations from the rich information in raw videos and text, directly optimized over user-item interactions, instead of using embeddings pretrained on proxy pretext task. In addition, we further exploit multimodal alignment of the item contents in a self-supervised manner, revealing great potential in content representation learning. From extensive experiments on public benchmarks, we verify the effectiveness of our method, achieving state-of-the-art performance on cold-start movie recommendation.콜드 스타트 아이템 추천은 추천시스템 연구에서 오래된 문제 중 하나이다. 콜드 스타트 문제를 해결하기 위해 흔히 사용해온 방법은 컨텐츠 기반 접근 방식을 사용하는 것이지만, 영화 추천 시스템 분야에서는 원본 비디오 및 원문 설명 등에 내재된 풍부한 정보를 충분히 활용해오지 못했다. 본 논문에서 제안하는 콜드 스타트 추천 프레임워크에서는 원본 비디오와 텍스트의 풍부한 컨텐츠 정보를 기반으로 멀티모달 컨텐츠 표현을 학습하는 과정에서, 다른 태스크에 사전 학습된 임베딩을 활용하는 대신 유저-아이템 상호작용 정보를 이용하여 직접 임베딩을 최적화하는 방법을 제안한다. 더 나아가, 본 연구는 자기 지도 학습 방법을 통해 여러 모달리티로 표현되어 있는 아이템 컨텐츠를 고려함으로써 컨텐츠 표현 학습의 발전 가능성을 재조명한다. 최종적으로 주요 벤치마크 데이터셋에 대한 다양한 실험을 통해 본 연구에서 제안하는 방법론의 효과를 입증함과 동시에 콜드 스타트 영화 추천 분야에서 해당 분야 최고 성능을 보이는 사실을 확인하였다.Chapter 1. Introduction 1 Chapter 2. Related Work 7 Chapter 3. Problem Formulation and Notations 10 Chapter 4. Preliminary 12 Chapter 5. The Proposed Method 16 Chapter 6. Experimental Settings 24 Chapter 7. Results and Discussion 28 Chapter 8. Summary and Future Work 36 Bibliography 37 Abstract in Korean 45석

Deriving Item Features Relevance from Past User Interactions

Item-based recommender systems suggest products based on the similarities between items computed either from past user prefer- ences (collaborative filtering) or from item content features (content- based filtering). Collaborative filtering has been proven to outper- form content-based filtering in a variety of scenarios. However, in item cold-start, collaborative filtering cannot be used directly since past user interactions are not available for the newly added items. Hence, content-based filtering is usually the only viable option left. In this paper we propose a novel feature-based machine learning model that addresses the item cold-start problem by jointly exploit- ing item content features and past user preferences. The model learns the relevance of each content feature from the collaborative item similarity, hence allowing to embed collaborative knowledge into a purely content-based algorithm. In our experiments, the proposed approach outperforms classical content-based filtering on an enriched version of the Netflix dataset, showing that collabo- rative knowledge can be effectively embedded into content-based approaches and exploited in item cold-start recommendation

A Fuzzy-Based Personalized Recommender System for Local Businesses

On-line reviewing systems have become prevalent in our society. User-provided reviews of local businesses have provided rich information in terms of users' preferences regarding businesses and their interactions in reviewing systems; however, little is known about how the reviewing behaviors of users can benefit businesses in terms of suggesting potential collaboration opportunities. In the current study, we aim to build a recommendation system for businesses to provide suggestions for business collaboration. Based on historical data from Yelp that shows two businesses being reviewed by the same users within a same season, we were able to identify businesses that might attract the same customers in the future, and hence provide them with a collaboration suggestion. Our results suggest that the evidence - two businesses sharing reviews from same users - can provide recommendations for businesses to pursue future collaborative marketing opportunities