Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 201

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner. The use of RNNs provides impressive performance benefits over classical methods in session-based recommendations. In this work we introduce novel ranking loss functions tailored to RNNs in the recommendation setting. The improved performance of these losses over alternatives, along with further tricks and refinements described in this work, allow for an overall improvement of up to 35% in terms of MRR and Recall@20 over previous session-based RNN solutions and up to 53% over classical collaborative filtering approaches. Unlike data augmentation-based improvements, our method does not increase training times significantly. We further demonstrate the performance gain of the RNN over baselines in an online A/B test.Comment: CIKM'18, authors' versio

Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions

Detecting controversy in general web pages is a daunting task, but increasingly essential to efficiently moderate discussions and effectively filter problematic content. Unfortunately, controversies occur across many topics and domains, with great changes over time. This paper investigates neural classifiers as a more robust methodology for controversy detection in general web pages. Current models have often cast controversy detection on general web pages as Wikipedia linking, or exact lexical matching tasks. The diverse and changing nature of controversies suggest that semantic approaches are better able to detect controversy. We train neural networks that can capture semantic information from texts using weak signal data. By leveraging the semantic properties of word embeddings we robustly improve on existing controversy detection methods. To evaluate model stability over time and to unseen topics, we asses model performance under varying training conditions to test cross-temporal, cross-topic, cross-domain performance and annotator congruence. In doing so, we demonstrate that weak-signal based neural approaches are closer to human estimates of controversy and are more robust to the inherent variability of controversies

How Consistent is Relevance Feedback in Exploratory Search?

Search activities involving knowledge acquisition, investigation and synthesis are collectively known as exploratory search. Exploratory search is challenging for users, who may be unable to formulate search queries, have ill-defined search goals or may even struggle to understand search results. To ameliorate these difficulties, reinforcement learning-based information retrieval systems were developed to provide adaptive support to users. Reinforcement learning is used to build a model of user intent based on relevance feedback provided by the user. But how reliable is relevance feedback in this context? To answer this question, we developed a novel permutation-based metric for scoring the consistency of relevance feedback. We used this metric to perform a retrospective analysis of interaction data from lookup and exploratory search experiments. Our analysis shows that for lookup search relevance judgments are highly consistent, supporting previous findings that relevance feedback improves retrieval performance. For exploratory search, however, the distribution of consistency scores shows considerable inconsistency.Peer reviewe

Engineering a Simplified 0-Bit Consistent Weighted Sampling

The Min-Hashing approach to sketching has become an important tool in data analysis, information retrial, and classification. To apply it to real-valued datasets, the ICWS algorithm has become a seminal approach that is widely used, and provides state-of-the-art performance for this problem space. However, ICWS suffers a computational burden as the sketch size K increases. We develop a new Simplified approach to the ICWS algorithm, that enables us to obtain over 20x speedups compared to the standard algorithm. The veracity of our approach is demonstrated empirically on multiple datasets and scenarios, showing that our new Simplified CWS obtains the same quality of results while being an order of magnitude faster

과거 가격 및 희소한 트윗을 이용한 주가 변동 예측

학위논문(석사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2021.8. 강유.Given historical stock prices and sparse tweets mentioning the stocks to predict, how can we precisely predict stock price movement? Many market analysts strive to use a large amount of information for prediction. However, they confront more noise when utilizing larger data for prediction. Thus, existing methods use only historical prices, or those along with a small amount of refined data such as news articles or tweets mentioning target stocks. However, they have the following limitations: 1) using only historical prices gives low performance since they have insufficient information, 2) news articles lack timeliness compared to social medias for predicting stock price movement, and 3) the previous methods using tweets do not handle stocks without tweets mentioning them. In this paper, we propose GLT (Stock Price Movement Prediction using Global and Local Trends of Tweets), an accurate stock price movement prediction method that works without tweets mentioning target stocks. GLT pre-trains both of stock and tweet representations in a self-supervised way. Then, GLT generates global and local tweet trends which represent global public opinion and the local trends related to target stocks, respectively. The trend vectors are combined to accurately predict stock price movement. Experimental results show that GLT provides the state-ofthe-art accuracy for stock price movement prediction.과거 주가와 예측할 주식을 언급하는 희소한 트윗이 주어졌을 때 주가 변동을 어떻게 정확하게 예측할 수 있을까? 많은 시장 분석가들은 예측을 위해 많은 양의 정 보를 사용하려고 노력합니다. 하지만 예측을 위해 더 많은 양의 데이터를 사용할수 록 더 많은 노이즈에 직면합니다. 따라서 기존 방법은 과거 주식 가격만 사용하거나 뉴스 기사 혹은 대상 주식을 언급하는 트윗과 같은 소량의 정제된 데이터를 사용 합니다. 그러나 기존 방법들은 다음과 같은 한계가 있습니다: 1) 과거 주식 가격만 사용하면 정보가 부족하여 성능이 저하되고, 2) 뉴스 기사는 주가 변동을 예측하는 데 소셜 미디어에 비해 적시성이 부족하며, 3) 트윗을 사용하는 이전 방법들은 트윗이 언급하지 않은 주식들을 처리하지 못합니다. 본 논문에서는 목표 주식을 언급하는 트윗 없이도 작동하는 정확한 주가 변동 예측 방법인 GLT (Stock Price Movement Prediction using Global and Local Trends of Tweets)를 제안합니다. GLT는 자가 감독 방식을 활용하여 주식 및 트윗 임베딩을 사전 학습합니다. 그런 다음 GLT는 각각 글로벌 여론과 목표 주식과 관련된 트렌드 를 나타내는 글로벌 및 로컬 트윗 트렌드를 생성합니다. 이러한 추세 벡터들은 주가 변동을 정확하게 예측하는데 기여합니다. 실험 결과에 따르면 GLT는 주가 변동 예 측에서 최고 수준의 정확도를 제공합니다.I. Introduction 1 II. Related Work 4 2.1 Stock Price Movement Prediction 4 2.2 Attentive LSTM 4 III. Proposed Method 6 3.1 Overview 6 3.2 Self-supervised Pre-training for Representing Tweets and Stocks 7 3.3 Global Tweet Trend 10 3.4 Local Tweet Trend 11 3.5 Stock Movement Prediction 11 IV. Experiment 13 4.1 Experiment Setting 13 4.2 Classification Performance 15 4.3 Ablation Study 16 4.4 Hyperparameter Robustness 16 V. Conclusion 18 References 19 Abstract in Korean 23석