OpenRaiser/Intern-Atlas huggingface dataset:
Improving Latent Factor Models via Personalized Feature Projection for One Class Recommendation
Authors: Tong Zhao, Julian J. McAuley, Irwin King
Venue: CIKM
Year: 2015

Latent Factor models, which transform both users and items into the same latent feature space, are one of the most successful and ubiquitous models in recommender systems. Most existing models in this paradigm define both users' and items' latent factors to be of the same size and use an inner product to represent a user's "compatibility" with an item. Intuitively, users' factors encode "preferences" while item factors encode "properties", so that the inner product encodes how well an item matches a user's preferences. However, a user's opinion of an item may be more complex, for example each dimension of each user's opinion may depend on a combination of multiple item factors simultaneously. Thus it may be better to view each dimension of a user's preference as a personalized projection of an item's properties so that the preference model can capture complex relationships between items' properties and users' preferences. Therefore, in this paper we propose a novel personalized feature projection method to model users' preferences over items. Specifically, for each user, we define a personalized projection matrix, which takes the place of user-specific factors from existing models. This matrix describes a mapping between items' factors and users' preferences in order to build personalized preference models for each user and item. The proposed personalized feature projection method is quite general and existing latent factor models, for example, can be cast as a special case. We present three objective functions to optimize predictions in the form of ranked lists of users' preferences over items, and demonstrate how each can be used to improve one-class recommendation performance. Experiments are conducted on four real-world datasets and our results show that our personalized feature projection method outperforms several state-of-the-art methods on various evaluation metrics.
Exploiting Game Theoretic Analysis for Link Recommendation in Social Networks
Authors: Tong Zhao, H. Vicky Zhao, Irwin King
Venue: CIKM
Year: 2015

The popularity of Online Social Networks (OSNs) has attracted great research interests in different fields. In Economics, researchers use game theory to analyze the mechanism of network formation, which is called Network Formation Game. While in Computer Science, much effort has been done in building machine learning models to predict future or missing links. However, there are few works considering how to combine game theoretic analysis and machine learning models. Therefore, in this paper, we study the problem of Exploiting Game Theoretic Analysis for Link Recommendation in Social Networks. Our goal is to improve link recommendation accuracy via leveraging the power of Network Formation Games into machine learning models. We present two different approaches to solve this problem. First, we propose a three- phase method that straightforwardly combines game theoretic analysis with machine learning models. Second, we develop a unified model, BPRLGT, that incorporates Network Formation Game into a Bayesian ranking framework for link recommendation. Specifically, BPRLGT takes advantage of network topology and we design a game theoretic sampling approach to improve its training process. The experiments are conducted on four real world datasets and the results on all datasets demonstrate that both our proposed three-phase method and the unified ranking model outperform the baseline methods.
Location-Based Influence Maximization in Social Networks
Authors: Tao Zhou, Jiuxin Cao, Bo Liu, Shuai Xu, Ziqing Zhu, Junzhou Luo
Venue: CIKM
Year: 2015

In this paper, we aim at the product promotion in O2O model and carry out the research of location-based influence maximization on the platform of LBSN. As offline consuming behavior exists under the O2O environment, the traditional online influence diffusion model could not describe the product acceptance accurately. Moreover, the existing researches of influence maximization tend to only concern on the online network of relationships but rarely take the offline part into consideration. This paper introduces the location property into the influence maximization to accord with the characteristic of O2O model. Firstly, we propose an improved influence diffusion model called TP Model which could accurately describe the process of accepting products under the O2O environment. Meanwhile, the definition of location-based influence maximization is presented. Then the user mobility pattern is analyzed and the calculation method of offline probability is designed. Considering the influence ability, a location-based influence maximization algorithm named TPH is proposed. Experiments prove TPH algorithm has general advantage. Finally, focusing on the performance of TPH algorithm under special circumstances, MR algorithm is designed as complement and experiments also verify its high effectiveness.
Making Sense of Spatial Trajectories
Authors: Xiaofang Zhou, Kai Zheng, Hoyoung Jeung, Jiajie Xu, Shazia Sadiq
Venue: CIKM
Year: 2015

Spatial trajectory data is widely available today. Over a sustained period of time, trajectory data has been collected from numerous GPS devices, smartphones, sensors and social media applications. Daily increases of real-time trajectory data have also been phenomenal in recent years. More and more new applications have emerged to derive business values from both trajectory data warehouses and real-time trajectory data. Due to their very large volumes, their nature of streaming, their highly variable levels of data quality, as well as many possible links with other types of data, making sense of spatial trajectory data becomes one of the crucial areas for big data analytics. In this paper we will present a review of the extensive work in spatiotemporal data management and trajectory mining, and discuss new challenges and new opportunities in the context of new applications, focusing on recent advances in trajectory data management and trajectory mining from their foundations to high performance processing with modern computing infrastructure.
An Optimization Framework for Propagation of Query-Document Features by Query Similarity Functions
Authors: Maxim Zhukovskiy, Tsimafei Khatkevich, Gleb Gusev, Pavel Serdyukov
Venue: CIKM
Year: 2015

It is well known that a great number of query--document features which significantly improve the quality of ranking for popular queries, however, do not provide any benefit for new or rare queries since there is typically not enough data associated with those queries that is required to reliably compute the values of those features. It is a common practice to propagate the values of such features from popular to tail queries, if the queries are similar according to some predefined query similarity functions. In this paper, we propose new algorithms that facilitate and increase the effectiveness of this propagation. Given a query similarity function and a query--document relevance feature, we introduce two different approaches (linear weighting approach and tree-based approach) to learn a function of values of the similarity function and values of the feature for the similar queries w.r.t. the given document. The propagated value of the feature equals the value of the obtained function for the given query--document pair.
aptMTVL: Nailing Interactions in Multi-Task Multi-View Multi-Label Learning using Adaptive-basis Multilinear Factor Analyzers
Authors: Xiaoli Li, Jun Huan
Venue: CIKM
Year: 2016

We investigate a new direction of multi-task multi-view learning where we have data sets with multiple tasks, multiple views and multiple labels. We call this problem a multi-task multi-view multi-label learning problem or MTVL learning for short. There is a wide application of MTVL leaning where examples include Internet of Things, brain science, and document classification. In designing effective MTVL learning algorithms, we hypothesize that a key component is to "disentangle" interactions among tasks, views, and labels, or the Ütask-view-label interactions. For that purpose we have developed an adaptive-basis multilinear analyzers(aptMLFA) that utilizes a loading tensor to modulate interactions among multiple latent factors. With aptMLFA we designed a new MTVL learning algorithm, aptMTVL, and evaluated its performance on 3 real-world data sets. The experimental results demonstrated the effectiveness of our proposed method as compared to the state-of-the-art MTVL learning algorithm.
Analyzing Data Relevance and Access Patterns of Live Production Database Systems
Authors: Martin Boissier, Carsten Alexander Meyer, Timo Djürken, Jan Lindemann, Kathrin Mao, Pascal Reinhardt, Tim Specht, Tim Zimmermann, Matthias Uflacker
Venue: CIKM
Year: 2016

Access to real-world database systems and their workloads is an invaluable source of information for database researchers. However, usually such full access is not possible due to tracing overheads, data protection, or legal reasons. In this paper, we present a tool set to analyze and compare synthetic and real-world database workloads, their characteristics, and access patterns. This tool set processes SQL workload traces and collects fine-grained access information without requiring direct read access to the production system. To gain insights into large real-world systems, we traced a live production enterprise system of a Global 2000 company and compare it with the synthetic benchmarks TPC-C and TPC-E.
Fully Dynamic Shortest-Path Distance Query Acceleration on Massive Networks
Authors: Takanori Hayashi, Takuya Akiba, Ken-ichi Kawarabayashi
Venue: CIKM
Year: 2016

The distance between vertices is one of the most fundamental measures for representing relations between them, and it is the basis of other classic measures of vertices, such as similarity, centrality, and influence. The 2-hop labeling methods are known as the fastest exact point-to-point distance algorithms on million-scale networks. However, they cannot handle billion-scale networks because of the large space requirement and long preprocessing time. In this paper, we present the first algorithm that can process exact distance queries on fully dynamic billion-scale networks besides trivial non-indexing algorithms, which combines an online bidirectional breadth-first search (BFS) and an offline indexing method for handling billion-scale networks in memory. First, we accelerate bidirectional BFSs by using heuristics that exploit the small-world property of complex networks. Then, we construct bit-parallel shortest-path trees to maintain sets of shortest paths passing through high-degree vertices of networks in compact form, the information of which enables us to avoid visiting vertices with high degrees during bidirectional BFSs. Thus, the searches achieve considerable speedup. In addition, our index size reduction technique enables us to handle billion-scale networks in memory. Furthermore, we introduce dynamic update procedures of our data structure to handle fully dynamic networks. We evaluated the performance of the proposed method on real-world networks. In particular, on large-scale social networks with over 1B edges, the proposed method enables us to answer distance queries in around 1 ms, on average.
Q+Tree: An Efficient Quad Tree based Data Indexing for Parallelizing Dynamic and Reverse Skylines
Authors: Md. Saiful Islam, Chengfei Liu, J. Wenny Rahayu, Tarique Anwar
Venue: CIKM
Year: 2016

Skyline queries play an important role in multi-criteria decision making applications of many areas. Given a dataset of objects, a skyline query retrieves data objects that are not dominated by any other data object in the dataset. Unlike standard skyline queries where the different aspects of data objects are compared directly, dynamic and reverse skyline queries adhere to the around-by semantics, which is realized by comparing the relative distances of the data objects w.r.t. a given query. Though, there are a number of works on parallelizing the standard skyline queries, only a few works are devoted to the parallel computation of dynamic and reverse skyline queries. This paper presents an efficient quad-tree based data indexing scheme, called Q+Tree, for parallelizing the computations of the dynamic and reverse skyline queries. We compare the performance of Q+Tree with an existing quad-tree based indexing scheme. We also present several optimization heuristics to improve the performance of both of the indexing schemes further. Experimentation with both real and synthetic datasets verifies the efficiency of the proposed indexing scheme and optimization heuristics.
A Multiple Instance Learning Framework for Identifying Key Sentences and Detecting Events
Authors: Wei Wang, Yue Ning, Huzefa Rangwala, Naren Ramakrishnan
Venue: CIKM
Year: 2016

State-of-the-art event encoding approaches rely on sentence or phrase level labeling, which are both time consuming and infeasible to extend to large scale text corpora and emerging domains. Using a multiple instance learning approach, we take advantage of the fact that while labels at the sentence level are difficult to obtain, they are relatively easy to gather at the document level. This enables us to view the problems of event detection and extraction in a unified manner. Using distributed representations of text, we develop a multiple instance formulation that simultaneously classifies news articles and extracts sentences indicative of events without any engineered features. We evaluate our model in its ability to detect news articles about civil unrest events (from Spanish text) across ten Latin American countries and identify the key sentences pertaining to these events. Our model, trained without annotated sentence labels, yields performance that is competitive with selected state-of-the-art models for event detection and sentence identification. Additionally, qualitative experimental results show that the extracted event-related sentences are informative and enhance various downstream applications such as article summarization, visualization, and event encoding.
Toward Data-Driven Education: CIKM-2016 Keynote
Authors: Rakesh Agrawal
Venue: CIKM
Year: 2016

A program of study can be viewed as a knowledge graph consisting of learning units and relationships between them. Such a knowledge graph provides the core data structure for organizing and navigating learning experiences. We address three issues in this talk. First, how can we synthesize the knowledge graph, given a set of concepts to be covered in the study program. Next, how can we use data mining to identify and correct deficiencies in a knowledge graph. Finally, how can we use data mining to form study groups with the goal of maximizing overall learning. We conclude by pointing out some open research problems.
A Fatigue Strength Predictor for Steels Using Ensemble Data Mining: Steel Fatigue Strength Predictor
Authors: Ankit Agrawal, Alok N. Choudhary
Venue: CIKM
Year: 2016

Fatigue strength is one of the most important mechanical properties of steel. High cost and time for fatigue testing, and potentially disastrous consequences of fatigue failures motivates the development of predictive models for this property. We have developed advanced data-driven ensemble predictive models for this purpose with an extremely high cross-validated accuracy of >98\%, and have deployed these models in a user-friendly online web-tool, which can make very fast predictions of fatigue strength for a given steel represented by its composition and processing information. Such a tool with fast and accurate models is expected to be a very useful resource for the materials science researchers and practitioners to assist in their search for new and improved quality steels. The web-tool is available at http://info.eecs.northwestern.edu/SteelFatigueStrengthPredictor
The Role of Relevance in Sponsored Search
Authors: Luca Maria Aiello, Ioannis Arapakis, Ricardo Baeza-Yates, Xiao Bai, Nicola Barbieri, Amin Mantrach, Fabrizio Silvestri
Venue: CIKM
Year: 2016

Sponsored search aims at retrieving the advertisements that in the one hand meet users' intent reflected in their search queries, and in the other hand attract user clicks to generate revenue. Advertisements are typically ranked based on their expected revenue that is computed as the product between their predicted probability of being clicked (i.e., namely clickability) and their advertiser provided bid. The relevance of an advertisement to a user query is implicitly captured by the predicted clickability of the advertisement, assuming that relevant advertisements are more likely to attract user clicks. However, this approach easily biases the ranking toward advertisements having rich click history. This may incorrectly lead to showing irrelevant advertisements whose clickability is not accurately predicted due to lack of click history. Another side effect consists of never giving a chance to new advertisements that may be highly relevant to be printed due to their lack of click history. To address this problem, we explicitly measure the relevance between an advertisement and a query without relying on the advertisement's click history, and present different ways of leveraging this relevance to improve user search experience without reducing search engine revenue. Specifically, we propose a machine learning approach that solely relies on text-based features to measure the relevance between an advertisement and a query. We discuss how the introduced relevance can be used in four important use cases: pre-filtering of irrelevant advertisements, recovering advertisements with little history, improving clickability prediction, and re-ranking of the advertisements on the final search result page. Offine experiments using large-scale query logs and online A/B tests demonstrate the superiority of the proposed click-oblivious relevance model and the important roles that relevance plays in sponsored search.
Hierarchical and Dynamic k-Path Covers
Authors: Takuya Akiba, Yosuke Yano, Naoto Mizuno
Venue: CIKM
Year: 2016

A metric-independent data structure for spatial networks called k-all-path cover (k-APC) has recently been proposed. It involves a set of vertices that covers all paths of size k, and is a general indexing technique that can accelerate various path-related processes on spatial networks, such as route planning and path subsampling to name a few. Although it is a promising tool, it currently has drawbacks pertaining to its construction and maintenance. First, k-APCs, especially for large values of k, are computationally too expensive. Second, an important factor related to quality is ignored by a prevalent construction algorithm. Third, an existing algorithm only focuses on static networks.
Content-Agnostic Malware Detection in Heterogeneous Malicious Distribution Graph
Authors: Ibrahim M. Alabdulmohsin, Yufei Han, Yun Shen, Xiangliang Zhang
Venue: CIKM
Year: 2016

Malware detection has been widely studied by analysing either file dropping relationships or characteristics of the file distribution network. This paper, for the first time, studies a global heterogeneous malware delivery graph fusing file dropping relationship and the topology of the file distribution network. The integration offers a unique ability of structuring the end-to-end distribution relationship. However, it brings large heterogeneous graphs to analysis. In our study, an average daily generated graph has more than 4 million edges and 2.7 million nodes that differ in type, such as IPs, URLs, and files. We propose a novel Bayesian label propagation model to unify the multi-source information, including content-agnostic features of different node types and topological information of the heterogeneous network. Our approach does not need to examine the source codes nor inspect the dynamic behaviours of a binary. Instead, it estimates the maliciousness of a given file through a semi-supervised label propagation procedure, which has a linear time complexity w.r.t. the number of nodes and edges. The evaluation on 567 million real-world download events validates that our proposed approach efficiently detects malware with a high accuracy.
SemiNMF-PCA framework for Sparse Data Co-clustering
Authors: Kais Allab, Lazhar Labiod, Mohamed Nadif
Venue: CIKM
Year: 2016

Several studies have demonstrated the importance of co-clustering which aims to cluster simultaneously the sets of objects and features. The co-clustering is often more effective than one-side clustering, especially when considering sparse high dimensional data. In this paper, we propose a novel way to consider the co-clustering and the reduction of the dimension simultaneously. Our approach takes advantage of the mutual reinforcement between Principal Component Analysis (PCA) which provides a low-dimensional representation of data and Semi-Nonnegative Matrix Factorization (SemiNMF) that learns this low-dimensional representation and lends itself to a co-clustering interpretation. In other words, the proposed framework aims to find an optimal subspace of multi-dimensional variables for effectively identifying a partition of the set of objects. We show that by doing so, our model is able to learn low-dimensional representations that are better suited for co-clustering, outperforming not only spectral methods, but also co-clustering graph-regularized-based methods.
Discriminative View Learning for Single View Co-Training
Authors: Joseph St. Amand, Jun Huan
Venue: CIKM
Year: 2016

Co-training, a popular semi-supervised learning technique, is severely limited as it applicable only to datasets which have a natural division of the feature space into two or more distinct views. In this paper, we investigate techniques to apply co-training to single-view data sets. We develop a view learning technique which takes a single view dataset and learns multiple views. These learned views balance the available discriminatory information in the dataset, while still meeting Blum's co-training criteria. In addition, we constrain the views such that pairs of learned view embedding functions exhibit sparsity in a complementary pattern, which aid in increasing diversity. Finally, we demonstrate the efficacy of our approach via experimental means on several real-world datasets from different domains.
A Probabilistic Fusion Framework
Authors: Yael Anava, Anna Shtok, Oren Kurland, Ella Rabinovich
Venue: CIKM
Year: 2016

There are numerous methods for fusing document lists retrieved from the same corpus in response to a query. Many of these methods are based on seemingly unrelated techniques and heuristics. Herein we present a probabilistic framework for the fusion task. The framework provides a formal basis for deriving and explaining many fusion approaches and the connections between them. Instantiating the framework using various estimates yields novel fusion methods, some of which significantly outperform state-of-the-art approaches.