Personalized Video Recommendation Using Rich Contents from Videos

Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods

Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation

Hashing is an effective technique to address the large-scale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items. However, existing hashing-based recommendation methods still suffer from two important problems: 1) Their recommendation process mainly relies on the user-item interactions and single specific content feature. When the interaction history or the content feature is unavailable (the cold-start problem), their performance will be seriously deteriorated. 2) Existing methods learn the hash codes with relaxed optimization or adopt discrete coordinate descent to directly solve binary hash codes, which results in significant quantization loss or consumes considerable computation time. In this paper, we propose a fast cold-start recommendation method, called Multi-Feature Discrete Collaborative Filtering (MFDCF), to solve these problems. Specifically, a low-rank self-weighted multi-feature fusion module is designed to adaptively project the multiple content features into binary yet informative hash codes by fully exploiting their complementarity. Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations. Experiments on two public recommendation datasets demonstrate that MFDCF outperforms the state-of-the-arts on various aspects

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

DeepStore: an interaction-aware Wide&Deep model for store site recommendation with attentional spatial embeddings

International audienceStore site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource data in cities has fostered unprecedented opportunities to the data-driven store site recommendation, which aims at leveraging large-scale user-generated data to analyze and mine users' preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single data source which lacks some significant data (e.g., consumption data and user profile data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different users at the store based on multisource data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low-and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource data, we propose two embedding methods for different types of data: 1) the filed embedding and 2) attention-based spatial embedding. Extensive experiments are conducted on a real-world dataset including store data, user data, and point-of-interest data, the results demonstrate that DeepStore outperforms the state-of-the-art models

Personalized Video Recommendation Using Rich Contents from Videos

© 1989-2012 IEEE. Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods