OpenRaiser/Intern-Atlas huggingface dataset:
Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder
Authors: Xu Zhao, Yi Ren, Ying Du, Shenzheng Zhang, Nian Wang
Venue: Conference SIGIR 2022
Year: 2022

Embedding & MLP has become a paradigm for modern large-scale recommendation system. However, this paradigm suffers from the cold-start problem which will seriously compromise the ecological health of recommendation systems. This paper attempts to tackle the item cold-start problem by generating enhanced warmed-up ID embeddings for cold items with historical data and limited interaction records. From the aspect of industrial practice, we mainly focus on the following three points of item cold-start: 1) How to conduct cold-start without additional data requirements and make strategy easy to be deployed in online recommendation scenarios. 2) How to leverage both historical records and constantly emerging interaction data of new items. 3) How to model the relationship between item ID and side information stably from interaction data. To address these problems, we propose a model-agnostic Conditional Variational Autoencoder based Recommendation(CVAR) framework with some advantages including compatibility on various backbones, no extra requirements for data, utilization of both historical data and recent emerging interactions. CVAR uses latent variables to learn a distribution over item side information and generates desirable item ID embeddings using a conditional decoder. The proposed method is evaluated by extensive offline experiments on public datasets and online A/B tests on Tencent News recommendation platform, which further illustrate the advantages and robustness of CVAR.
Recommendation for new users and new items via randomized training and mixture-of-experts transformation
Authors: Ziwei Zhu 0001, Shahin Sefati, Parsa Saadatpanah, James Caverlee
Venue: Conference SIGIR 2020
Year: 2020

The cold start problem is a long-standing challenge in recommender systems. That is, how to recommend for new users and new items without any historical interaction record? Recent ML-based approaches have made promising strides versus traditional methods. These ML approaches typically combine both user-item interaction data of existing warm start users and items (as in CF-based methods) with auxiliary information of users and items such as user profiles and item content information (as in content-based methods). However, such approaches face key drawbacks including the error superimposition issue that the auxiliary-to-CF transformation error increases the final recommendation error; the ineffective learning issue that long distance from transformation functions to model output layer leads to ineffective model learning; and the unified transformation issue that applying the same transformation function for different users and items results in poor transformation.
Diversification and refinement in collaborative filtering recommender
Authors: Rubi Boim, Tova Milo, Slava Novgorodov
Venue: Conference CIKM 2011
Year: 2011

This paper considers a popular class of recommender systems that are based on Collaborative Filtering (CF) and proposes a novel technique for diversifying the recommendations that they give to users. Items are clustered based on a unique notion of priority-medoids that provides a natural balance between the need to present highly ranked items vs. highly diverse ones. Our solution estimates items diversity by comparing the rankings that different users gave to the items, thereby enabling diversification even in common scenarios where no semantic information on the items is available. It also provides a natural zoom-in mechanism to focus on items (clusters) of interest and recommending diversified similar items. We present DiRec a plug-in that implements the above concepts and allows CF Recommender systems to diversify their recommendations. We illustrate the operation of DiRec in the context of a movie recommendation system and present a thorough experimental study that demonstrates the effectiveness of our recommendation diversification technique and its superiority over previous solutions.
Personalized Flight Itinerary Ranking at Fliggy
Authors: Jinhong Huang, Yang Li, Shan Sun, Bufeng Zhang, Jin Huang
Venue: Conference CIKM 2020
Year: 2020

Flight itinerary ranking is critical for Online Travel Agencies (OTAs) since more and more customers book flights online. Currently, most OTAs still adopt rule-based strategies. However, rule-based methods are not able to model context-aware information and user preferences. To this end, a novel Personalized Flight itinerary Ranking Network (PFRN) is proposed in this paper. In PFRN, a Listwise Feature Encoding (LFE) structure is proposed to capture global context-aware information and mutual influences among inputs. Besides, we utilize behaviors of both individual user and group users sharing the same intention to express user preferences. Then a User Attention Mechanism is proposed to rank flight itineraries based on the user preferences effectively and efficiently. Offline experiments on real-world datasets from Amadeus and Fliggy show the superior performance of the proposed PFRN. Moreover, PFRN has been successfully deployed on online system for searching itineraries at Fliggy and achieved significant improvements.
An Automatic Weighting Scheme for Collaborative Filtering
Authors: Rong Jin, Joyce Y. Chai, Luo Si
Venue: Conference SIGIR 2004
Year: 2004

Collaborative filtering identifies information interest of a particular user based on the information provided by other similar users. The memory-based approaches for collaborative filtering (e.g., Pearson correlation coefficient approach) identify the similarity between two users by comparing their ratings on a set of items. In these approaches, different items are weighted either equally or by some predefined functions. The impact of rating discrepancies among different users has not been taken into consideration. For example, an item that is highly favored by most users should have a smaller impact on the user-similarity than an item for which different types of users tend to give different ratings. Even though simple weighting methods such as variance weighting try to address this problem, empirical studies have shown that they are ineffective in improving the performance of collaborative filtering. In this paper, we present an optimization algorithm to automatically compute the weights for different items based on their ratings from training users. More specifically, the new weighting scheme will create a clustered distribution for user vectors in the item space by bringing users of similar interests closer and separating users of different interests more distant. Empirical studies over two datasets have shown that our new weighting scheme substantially improves the performance of the Pearson correlation coefficient method for collaborative filtering.
Surrogate for Long-Term User Experience in Recommender Systems
Authors: Yuyan Wang, Mohit Sharma 0002, Can Xu, Sriraj Badam, Qian Sun 0005, Lee Richardson, Lisa Chung, Ed H. Chi, Minmin Chen
Venue: Conference KDD 2022
Year: 2022

Over the years we have seen recommender systems shifting focus from optimizing short-term engagement toward improving long-term user experience on the platforms. While defining good long-term user experience is still an active research area, we focus on one specific aspect of improved long-term user experience here, which is user revisiting the platform. These long term outcomes however are much harder to optimize due to the sparsity in observing these events and low signal-to-noise ratio (weak connection) between these long-term outcomes and a single recommendation. To address these challenges, we propose to establish the association between these long-term outcomes and a set of more immediate term user behavior signals that can serve as surrogates for optimization.
Imrank: influence maximization via finding self-consistent ranking
Authors: Suqi Cheng, Huawei Shen, Junming Huang, Wei Chen, Xueqi Cheng
Venue: Conference SIGIR 2014
Year: 2014

Influence maximization, fundamental for word-of-mouth marketing and viral marketing, aims to find a set of seed nodes maximizing influence spread on social network. Early methods mainly fall into two paradigms with certain benefits and drawbacks: (1) Greedy algorithms, selecting seed nodes one by one, give a guaranteed accuracy relying on the accurate approximation of influence spread with high computational cost; (2) Heuristic algorithms, estimating influence spread using efficient heuristics, have low computational cost but unstable accuracy. We first point out that greedy algorithms are essentially finding a self-consistent ranking, where nodes' ranks are consistent with their ranking-based marginal influence spread. This insight motivates us to develop an iterative ranking framework, i.e., IMRank, to efficiently solve influence maximization problem under independent cascade model. Starting from an initial ranking, e.g., one obtained from efficient heuristic algorithm, IMRank finds a self-consistent ranking by reordering nodes iteratively in terms of their ranking-based marginal influence spread computed according to current ranking. We also prove that IMRank definitely converges to a self-consistent ranking starting from any initial ranking. Furthermore, within this framework, a last-to-first allocating strategy and a generalization of this strategy are proposed to improve the efficiency of estimating ranking-based marginal influence spread for a given ranking. In this way, IMRank achieves both remarkable efficiency and high accuracy by leveraging simultaneously the benefits of greedy algorithms and heuristic algorithms. As demonstrated by extensive experiments on large scale real-world social networks, IMRank always achieves high accuracy comparable to greedy algorithms, while the computational cost is reduced dramatically, about 10-100 times faster than other scalable heuristics.
Celf++ optimizing the greedy algorithm for influence maximization in social networks
Authors: Amit Goyal, Wei Lu, Laks V. S. Lakshmanan
Venue: Conference WWW 2011
Year: 2011

Kempe et al. [4] (KKT) showed the problem of influence maximization is NP-hard and a simple greedy algorithm guarantees the best possible approximation factor in PTIME. However, it has two major sources of inefficiency. First, finding the expected spread of a node set is #P-hard. Second, the basic greedy algorithm is quadratic in the number of nodes. The first source is tackled by estimating the spread using Monte Carlo simulation or by using heuristics[4, 6, 2, 5, 1, 3]. Leskovec et al. proposed the CELF algorithm for tackling the second. In this work, we propose CELF++ and empirically show that it is 35-55% faster than CELF.
Influence maximization over large-scale social networks: A bounded linear approach
Authors: Qi Liu, Biao Xiang, Enhong Chen, Hui Xiong, Fangshuang Tang, Jeffrey Xu Yu
Venue: Conference CIKM 2014
Year: 2014

Information diffusion in social networks is emerging as a promising solution to successful viral marketing, which relies on the effective and efficient identification of a set of nodes with the maximal social influence. While there are tremendous efforts on the development of social influence models and algorithms for social influence maximization, limited progress has been made in terms of designing both efficient and effective algorithms for finding a set of nodes with the maximal social influence. To this end, in this paper, we provide a bounded linear approach for influence computation and influence maximization. Specifically, we first adopt a linear and tractable approach to describe the influence propagation. Then, we develop a quantitative metric, named Group-PageRank, to quickly estimate the upper bound of the social influence based on this linear approach. More importantly, we provide two algorithms Linear and Bound, which exploit the linear approach and Group-PageRank for social influence maximization. Finally, extensive experimental results demonstrate that (a) the adopted linear approach has a close relationship with traditional models and Group-PageRank provides a good estimation of social influence; (b) Linear and Bound can quickly find a set of the most influential nodes and both of them are scalable for large-scale social networks.
Community-based greedy algorithm for mining top-k influential nodes in mobile social networks
Authors: Yu Wang, Gao Cong, Guojie Song, Kunqing Xie
Venue: Conference KDD 2010
Year: 2010

With the proliferation of mobile devices and wireless technologies, mobile social network systems are increasingly available. A mobile social network plays an essential role as the spread of information and influence in the form of "word-of-mouth". It is a fundamental issue to find a subset of influential individuals in a mobile social network such that targeting them initially (e.g. to adopt a new product) will maximize the spread of the influence (further adoptions of the new product). The problem of finding the most influential nodes is unfortunately NP-hard. It has been shown that a Greedy algorithm with provable approximation guarantees can give good approximation; However, it is computationally expensive, if not prohibitive, to run the greedy algorithm on a large mobile network.
Multi-Objective Ranking Optimization for Product Search Using Stochastic Label Aggregation
Authors: David Carmel, Elad Haramaty, Arnon Lazerson, Liane Lewin-Eytan
Venue: Conference WWW 2020
Year: 2020

Learning a ranking model in product search involves satisfying many requirements such as maximizing the relevance of retrieved products with respect to the user query, as well as maximizing the purchase likelihood of these products. Multi-Objective Ranking Optimization (MORO) is the task of learning a ranking model from training examples while optimizing multiple objectives simultaneously. Label aggregation is a popular solution approach for multi-objective optimization, which reduces the problem into a single objective optimization problem, by aggregating the multiple labels of the training examples, related to the different objectives, to a single label. In this work we explore several label aggregation methods for MORO in product search. We propose a novel stochastic label aggregation method which randomly selects a label per training example according to a given distribution over the labels. We provide a theoretical proof showing that stochastic label aggregation is superior to alternative aggregation approaches, in the sense that any optimal solution of the MORO problem can be generated by a proper parameter setting of the stochastic aggregation process. We experiment on three different datasets: two from the voice product search domain, and one publicly available dataset from the Web product search domain. We demonstrate empirically over these three datasets that MORO with stochastic label aggregation provides a family of ranking models that fully dominates the set of MORO models built using deterministic label aggregation.
COPR: Consistency-Oriented Pre-Ranking for Online Advertising
Authors: Zhishan Zhao, Jingyue Gao, Yu Zhang, Shuguang Han, Siyuan Lou, Xiang-Rong Sheng, Zhe Wang, Han Zhu, Yuning Jiang, Jian Xu, Bo Zheng
Venue: Conference CIKM 2023
Year: 2023

Cascading architecture has been widely adopted in large-scale advertising systems to balance efficiency and effectiveness. In this architecture, the pre-ranking model is expected to be a lightweight approximation of the ranking model, which handles more candidates with strict latency requirements. Due to the gap in model capacity, the pre-ranking and ranking models usually generate inconsistent ranked results, thus hurting the overall system effectiveness. The paradigm of score alignment is proposed to regularize their raw scores to be consistent. However, it suffers from inevitable alignment errors and error amplification by bids when applied in online advertising. To this end, we introduce a consistency-oriented pre-ranking framework for online advertising, which employs a chunk-based sampling module and a plug-and-play rank alignment module to explicitly optimize consistency of ECPM-ranked results. A $ΔNDCG$-based weighting mechanism is adopted to better distinguish the importance of inter-chunk samples in optimization. Both online and offline experiments have validated the superiority of our framework. When deployed in Taobao display advertising system, it achieves an improvement of up to +12.3\% CTR and +5.6\% RPM.
Efficient algorithms for public-private social networks
Authors: Flavio Chierichetti, Alessandro Epasto, Ravi Kumar, Silvio Lattanzi, Vahab S. Mirrokni
Venue: Conference KDD 2015
Year: 2015

We introduce the public-private model of graphs. In this model, we have a public graph and each node in the public graph has an associated private graph. The motivation for studying this model stems from social networks, where the nodes are the users, the public graph is visible to everyone, and the private graph at each node is visible only to the user at the node. From each node's viewpoint, the graph is just a union of its private graph and the public graph.
Fraudar: Bounding graph fraud in the face of camouflage
Authors: Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, Christos Faloutsos
Venue: Conference KDD 2016
Year: 2016

Given a bipartite graph of users and the products that they review, or followers and followees, how can we detect fake reviews or follows? Existing fraud detection methods (spectral, etc.) try to identify dense subgraphs of nodes that are sparsely connected to the remaining graph. Fraudsters can evade these methods using camouflage, by adding reviews or follows with honest targets so that they look "normal". Even worse, some fraudsters use hijacked accounts from honest users, and then the camouflage is indeed organic. Our focus is to spot fraudsters in the presence of camouflage or hijacked accounts. We propose FRAUDAR, an algorithm that (a) is camouflage-resistant, (b) provides upper bounds on the effectiveness of fraudsters, and (c) is effective in real-world data. Experimental results under various attacks show that FRAUDAR outperforms the top competitor in accuracy of detecting both camouflaged and non-camouflaged fraud. Additionally, in real-world experiments with a Twitter follower-followee graph of 1.47 billion edges, FRAUDAR successfully detected a subgraph of more than 4000 detected accounts, of which a majority had tweets showing that they used follower-buying services.
Predicting intraoperative hypoxemia with hybrid inference sequence autoencoder networks
Authors: Hanyang Liu, Michael Montana, Dingwen Li, Chase Renfroe, Thomas George Kannampallil, Chenyang Lu
Venue: Conference CIKM 2022
Year: 2022

We present an end-to-end model using streaming physiological time series to predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious patient harm during surgery. Inspired by the fact that a hypoxemia event is defined based on a future sequence of low SpO2 (i.e., blood oxygen saturation) instances, we propose the hybrid inference network (hiNet) that makes hybrid inference on both future low SpO2 instances and hypoxemia outcomes. hiNet integrates 1) a joint sequence autoencoder that simultaneously optimizes a discriminative decoder for label prediction, and 2) two auxiliary decoders trained for data reconstruction and forecast, which seamlessly learn contextual latent representations that capture the transition from present states to future states. All decoders share a memory-based encoder that helps capture the global dynamics of patient measurement. For a large surgical cohort of 72,081 surgeries at a major academic medical center, our model outperforms strong baselines including the model used by the state-of-the-art hypoxemia prediction system. With its capability to make real-time predictions of near-term hypoxemic at clinically acceptable alarm rates, hiNet shows promise in improving clinical decision making and easing burden of perioperative care.
Perioperative predictions with interpretable latent representation
Authors: Bing Xue 0003, York Jiao, Thomas George Kannampallil, Bradley A. Fritz, Christopher Ryan King, Joanna Abraham, Michael Avidan, Chenyang Lu 0001
Venue: Conference KDD 2022
Year: 2022

Given the risks and cost of hospitalization, there has been significant interest in exploiting machine learning models to improve perioperative care. However, due to the high dimensionality and noisiness of perioperative data, it remains a challenge to develop accurate and robust encoding for surgical predictions. Furthermore, it is important for the encoding to be interpretable by perioperative care practitioners to facilitate their decision making process. We proposeclinical variational autoencoder (cVAE), a deep latent variable model that addresses the challenges of surgical applications through two salient features. (1) To overcome performance limitations of traditional VAE, it isprediction-guided with explicit expression of predicted outcome in the latent representation. (2) Itdisentangles the latent space so that it can be interpreted in a clinically meaningful fashion. We apply cVAE to two real-world perioperative datasets to evaluate its efficacy and performance in predicting outcomes that are important to perioperative care, including postoperative complication and surgery duration. To demonstrate the generality and facilitate reproducibility, we also apply cVAE to the open MIMIC-III dataset for predicting ICU duration and mortality. Our results show that the latent representation provided by cVAE leads to superior performance in classification, regression and multi-task predictions. The two features of cVAE are mutually beneficial and eliminate the need of a predictor. We further demonstrate the interpretability of the disentangled representation and its capability to capture intrinsic characteristics of hospitalized patients. While this work is motivated by and evaluated in the context of clinical applications, the proposed approach may be generalized for other fields using high-dimensional and noisy data and valuing interpretable representations.
Joint knowledge pruning and recurrent graph convolution for news recommendation
Authors: Yu Tian 0008, Yuhao Yang 0002, Xudong Ren, Pengfei Wang 0009, Fangzhao Wu, Qian Wang 0002, Chenliang Li 0005
Venue: Conference SIGIR 2021
Year: 2021

Recently, exploiting a knowledge graph (KG) to enrich the semantic representation of a news article have been proven to be effective for news recommendation. These solutions focus on the representation learning for news articles with additional information in the knowledge graph, where the user representations are mainly derived based on these news representations later. However, different users would hold different interests on the same news article. In other words, directly identifying the entities relevant to the user's interest and deriving the resultant user representation could enable a better news recommendation and explanation.