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

Recommender Systems

We develop a behavioral model for liquidity and volatility based on empirical regularities in trading order flow in the London Stock Exchange. This can be viewed as a very simple agent based model in which all components of the model are validated against real data. Our empirical studies of order flow uncover several interesting regularities in the way trading orders are placed and cancelled. The resulting simple model of order flow is used to simulate price formation under a continuous double auction, and the statistical properties of the resulting simulated sequence of prices are compared to those of real data. The model is constructed using one stock (AZN) and tested on 24 other stocks. For low volatility, small tick size stocks (called Group I) the predictions are very good, but for stocks outside Group I they are not good. For Group I, the model predicts the correct magnitude and functional form of the distribution of the volatility and the bid-ask spread, without adjusting any parameters based on prices. This suggests that at least for Group I stocks, the volatility and heavy tails of prices are related to market microstructure effects, and supports the hypothesis that, at least on short time scales, the large fluctuations of absolute returns are well described by a power law with an exponent that varies from stock to stock.

An empirical behavioral model of liquidity and volatility

https://doc.rero.ch/record/306790/files/zha_rcn.pdf

Ranking in evolving complex networks

Dealing with the new user cold-start problem in recommender systems: A comparative review

Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Well-established ranking algorithms (such as the popular Google's PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. The recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of real network traffic, prediction of future links, and identification of highly-significant nodes.

Based on a hybrid algorithm incorporating the heat conduction and probability spreading processes (Proc. Natl. Acad. Sci. U.S.A., 107 (2010) 4511), in this letter, we propose an improved method by introducing an item-oriented function, focusing on solving the dilemma of the recommendation accuracy between the cold and popular items. Differently from previous works, the present algorithm does not require any additional information (e.g., tags). Further experimental results obtained in three real datasets, RYM, Netflix and MovieLens, show that, compared with the original hybrid method, the proposed algorithm significantly enhances the recommendation accuracy of the cold items, while it keeps the recommendation accuracy of the overall and the popular items. This work might shed some light on both understanding and designing effective methods for long-tailed online applications of recommender systems.

/pdf/an-item-oriented-recommendation-algorithm-on-cold-start-3mwquh7j0n.pdf

An item-oriented recommendation algorithm on cold-start problem

The cumulative distribution of trading volume is investigated for Chinese stocks. Different from the power-law scaling of mature markets, the distribution is well fitted by a stretched exponential function f(x)∼e−αxγ. With the autocorrelation function and the detrended fluctuation analysis, the long-range autocorrelation of trading volume is revealed. The conditional dependence of volume on volatility and the volume–volatility cross-correlation are studied, and a positive long-range correlation between volume and volatility is observed.

Statistical properties of trading volume of Chinese stocks

We investigate the probability distribution of the volatility return intervals τ for the Chinese stock market. We rescale both the probability distribution P q ( τ ) and the volatility return intervals τ as P q ( τ ) = 1 / τ ¯ f ( τ / τ ¯ ) to obtain a uniform scaling curve for different threshold value q . The scaling curve can be well fitted by the stretched exponential function f ( x ) ∼ e − α x γ , which suggests memory exists in τ . To demonstrate the memory effect, we investigate the conditional probability distribution P q ( τ | τ 0 ) , the mean conditional interval 〈 τ | τ 0 〉 and the cumulative probability distribution of the cluster size of τ . The results show clear clustering effect. We further investigate the persistence probability distribution P ± ( t ) and find that P − ( t ) decays by a power law with the exponent far different from the value 0.5 for the random walk, which further confirms long memory exists in τ . The scaling and long memory effect of τ for the Chinese stock market are similar to those obtained from the United States and the Japanese financial markets.

Scaling and memory effect in volatility return interval of the Chinese stock market

Redundant correlation effect on personalized recommendation

Finding a universal description of the algorithm optimization is one of the key challenges in personalized recommendation. In this article, for the first time, we introduce a scaling-based algorithm (SCL) independent of recommendation list length based on a hybrid algorithm of heat conduction and mass diffusion, by finding out the scaling function for the tunable parameter and object average degree. The optimal value of the tunable parameter can be abstracted from the scaling function, which is heterogeneous for the individual object. Experimental results obtained from three real datasets, Netflix, MovieLens and RYM, show that the SCL is highly accurate in recommendation. More importantly, compared with a number of excellent algorithms, including the mass diffusion method, the original hybrid method, and even an improved version of the hybrid method, the SCL algorithm remarkably promotes the personalized recommendation in three other aspects: solving the accuracy-diversity dilemma, presenting a high novelty, and solving the key challenge of cold start problem.

/pdf/information-filtering-via-a-scaling-based-function-3zo074xuy1.pdf

Guang Chen

Papers

An item-oriented recommendation algorithm on cold-start problem

Statistical properties of trading volume of Chinese stocks

Scaling and memory effect in volatility return interval of the Chinese stock market

Redundant correlation effect on personalized recommendation

Information filtering via a scaling-based function.