On the Predictability of Talk Attendance at Academic Conferences

This paper focuses on the prediction of real-world talk attendances at academic conferences with respect to different influence factors. We study the predictability of talk attendances using real-world tracked face-to-face contacts. Furthermore, we investigate and discuss the predictive power of user interests extracted from the users' previous publications. We apply Hybrid Rooted PageRank, a state-of-the-art unsupervised machine learning method that combines information from different sources. Using this method, we analyze and discuss the predictive power of contact and interest networks separately and in combination. We find that contact and similarity networks achieve comparable results, and that combinations of different networks can only to a limited extend help to improve the prediction quality. For our experiments, we analyze the predictability of talk attendance at the ACM Conference on Hypertext and Hypermedia 2011 collected using the conference management system Conferator

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Can Who-Edits-What Predict Edit Survival?

As the number of contributors to online peer-production systems grows, it becomes increasingly important to predict whether the edits that users make will eventually be beneficial to the project. Existing solutions either rely on a user reputation system or consist of a highly specialized predictor that is tailored to a specific peer-production system. In this work, we explore a different point in the solution space that goes beyond user reputation but does not involve any content-based feature of the edits. We view each edit as a game between the editor and the component of the project. We posit that the probability that an edit is accepted is a function of the editor's skill, of the difficulty of editing the component and of a user-component interaction term. Our model is broadly applicable, as it only requires observing data about who makes an edit, what the edit affects and whether the edit survives or not. We apply our model on Wikipedia and the Linux kernel, two examples of large-scale peer-production systems, and we seek to understand whether it can effectively predict edit survival: in both cases, we provide a positive answer. Our approach significantly outperforms those based solely on user reputation and bridges the gap with specialized predictors that use content-based features. It is simple to implement, computationally inexpensive, and in addition it enables us to discover interesting structure in the data.Comment: Accepted at KDD 201

협업필터링을 위한시간에 따라 변화하는 상품 특징 조건부 변분 자동 생성기

학위논문(석사)--서울대학교 대학원 :공과대학 산업공학과,2019. 8. 조성준.사용자가 상품 선택을 결정할 때 영향을 미치는 요소 중에는 시기적 요소와는 무관한 사용자 고유의 취향과 시점에 따라 변화하는 상품의 유행과 같은 외부족 요소가 존재한다. 정밀한 추천시스템을 만들기 위해서는 이 두 요소를 모두 고려해야 하지만 기존의 추천 시스템은 추천을 해주는 그 당시의 시점을 고려하지 않고 사용자의 구매내역 데이터만을 기반으로 추천을 해준다는 문제점을 갖고 있다. 따라서, 본 논문에서는 추천이 진행되는 시점을 고려하여, 그 당시의 상품 유행 요소를 반영한 추천 시스템을 제안한다. 우리는 상품 내용 기반의 추천 시스템과 사용자 구매 내역 기반의 추천시스템을 효과적으로 결합할 수 있는 모델 생성에 초점을 두었다. 구체적으로, 사용자-상품 내포 피드백에 상품의 시간 기반 특징을 더하기 위해 조건부 변분 자동 생성기 모델을 활용한다. 이 때 입력 값과 잠재변수에 각각 더해주는 조건부로는 각 시점의 상품 특징을 나타내는 분산 표현을 사용한다. 각 시점에 해당하는 분산 표현은 LSTM을 사용하여 추출한다. LSTM에서 얻은 조건부와 사용자-상품 행렬을 연결시켜 변분 자동 생성기에 입력해줌으로써, 각 구간의 중요한 특징을 반영하는 하이브리드 추천시스템을 생성한다. 본 논문에서 제안하는 모델은 상품 고유의 변화하는 특성을 반영하여 추천에 활용한다는 점에서 기존 연구와 차이가 있다. 또한, 본 연구는 추천시스템 데이터의 섬김성 문제를 완화하는데에도 도움을 준다. 제안하는 모델의 평가를 위해 Movielens dataset(ml-1m) 데이터와 Amazon women's clothing 데이터를 사용하였고, 제안하는 방법에 알맞은 실험 과정을 설계하여 모델의 유효성을 확인했다.We can assume that some factors that affect the user's decision to select products are several factors such as the time-invariant user's unique taste and the external trends or fashion that varies with time. Both mentioned factors should be considered in order to create a precise recommendation system, but the current recommendation system has the problem of making recommendations based only on the user's history without taking into account the timing of creating a recommendation. Therefore, considering the timing of the recommendation made, this paper proposes a recommendation system that reflects inter-items trends of time-based bin. We focus on creating a model that could effectively combine the content-based recommender system with the context-based recommender system. Specifically, we use Conditional Variational Autoencoder to add a time dynamic item features to user-item implicit feedback data. In this case, distributed representation of items in the specific period is used as a condition that is added to input and latent variable of VAE respectively. The distributed representation per periods can be extracted using LSTM. By putting a condition into VAE, a hybrid recommendation system can be created to reflect the item trend. The model proposed in this paper differs from existing research in that it reflects the changing characteristics inherent in the product and utilizes it in the recommendation. In addition, we can get the additional effect of solving sparsity problem by using item feature to mitigate sparsity problem. The Movielens data (ml-1m) data and Amazon women's clothing dataset are used for the evaluation of the proposed model. The effectiveness of this model is verified by designing experimental methods to evaluate the recommended systems that reflect the time point of recommendation.1.Introduction 1.1 Problem description 1.2 Research motivation and contribution 1.3 Organization of the thesis 2. Literature review 2.1 Review on neural network models 2.1.1 LSTM 2.1.2 Variation Autoencoder 2.1.3 Conditional Variational Autoencoder 2.2 Review on recommender system 3. Solution approaches 3.1 Content feature encoder - Long Short Term Memory 3.2 User-Item matrix generator - conditional variation autoencoder 3.2.1 Conditional variational autoencoder 3.2.2 Attention conditional variational autoencoder 3.3 Process of training 4. Experimental procedure 4.1 Evaluation datasets 4.2 Experimental setting 4.3 Evaluation metrics 4.4 Baseline 4.5 Results and discussion 5. Conclusion 5.1 Conclusion 5.2 Future directionMaste

Adversarial Training Towards Robust Multimedia Recommender System

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR.Comment: TKD