Hongke Zhao

Portfolio Selections in P2P Lending: A Multi-Objective Perspective

P2P lending is an emerging wealth-management service for individuals, which allows lenders to directly bid and invest on the loans created by borrowers. In these platforms, lenders often pursue multiple objectives (e.g., non-default probability, fully-funded probability and winning-bid probability) when they select loans to invest. How to automatically assess loans from these objectives and help lenders select loan portfolios is a very important but challenging problem. To that end, in this paper, we present a holistic study on portfolio selections in P2P lending. Specifically, we first propose to adapt gradient boosting decision tree, which combines both static features and dynamic features, to assess loans from multiple objectives. Then, we propose two strategies, i.e., weighted objective optimization strategy and multi-objective optimization strategy, to select portfolios for lenders. For each lender, the first strategy attempts to provide one optimal portfolio while the second strategy attempts to provide a Pareto-optimal portfolio set. Further, we design two algorithms, namely DPA and EVA, which can efficiently resolve the optimizations in these two strategies, respectively. Finally, extensive experiments on a large-scale real-world data set demonstrate the effectiveness of our solutions.

A Sequential Approach to Market State Modeling and Analysis in Online P2P Lending

Online peer-to-peer (P2P) lending is an emerging wealth-management service for individuals, which allows lenders to directly bid and invest on the listings created by borrowers without going through any traditional financial intermediaries. As a nonbank financial platform, online P2P lending tends to have both high volatility and liquidity. Therefore, it is of significant importance to discern the hidden market states of the listings (e.g., hot and cold), which open venues for enhancing business analytics and investment decision making. However, the problem of market state modeling remains pretty open due to many technical and domain challenges, such as the dynamic and sequential characteristics of listings. To that end, in this paper, we present a focused study on market state modeling and analysis for online P2P lending. Specifically, we first propose two enhanced sequential models by extending the Bayesian hidden Markov model (BHMM), namely listing-BHMM (L-BHMM) and listing and marketing-BHMM (LM-BHMM), for learning the latent semantics between listings’ market states and lenders’ bidding behaviors. Particularly, L-BHMM is a straightforward model that only considers the local observations of a listing itself, while LM-BHMM considers not only the listing information but also the global information of current market (e.g., the competitive and complementary relations among listings). Furthermore, we demonstrate several motivating applications enabled by our models, such as bidding prediction and herding detection. Finally, we construct extensive experiments on two real-world data sets and make some deep analysis on bidding behaviors, which clearly validate the effectiveness of our models in terms of different applications and also reveal some interesting business findings.

P2P Lending Survey: Platforms, Recent Advances and Prospects

P2P lending is an emerging Internet-based application where individuals can directly borrow money from each other. The past decade has witnessed the rapid development and prevalence of online P2P lending platforms, examples of which include Prosper, LendingClub, and Kiva. Meanwhile, extensive research has been done that mainly focuses on the studies of platform mechanisms and transaction data. In this article, we provide a comprehensive survey on the research about P2P lending, which, to the best of our knowledge, is the first focused effort in this field. Specifically, we first provide a systematic taxonomy for P2P lending by summarizing different types of mainstream platforms and comparing their working mechanisms in detail. Then, we review and organize the recent advances on P2P lending from various perspectives (e.g., economics and sociology perspective, and data-driven perspective). Finally, we propose our opinions on the prospects of P2P lending and suggest some future research directions in this field. Meanwhile, throughout this paper, some analysis on real-world data collected from Prosper and Kiva are also conducted.

Finding potential lenders in P2P lending: A Hybrid Random Walk Approach

Abstract P2P lending is a burgeoning online service that allows individuals to directly borrow money from each other. In these platforms, each loan has a specific duration for raising money from lenders. Following the “all-or-nothing” rule, many loans fail due to insufficient pledges/money in their funding durations. Thus, automatically accessing and finding potential lenders early is crucial for loans. However, this problem has some unique challenges (e.g., the temporality of loan) that are still being explored. To that end, in this paper, we present a holistic study on finding potential lenders in P2P lending. Specifically, we propose a hybrid random walk approach, i.e., RWH , by combining both collaborative filtering and content-based filtering, which can be adapted to loans at any funding progress (e.g., the starting progress). In the content-based filtering of RWH , the model extract dynamic features and adopt bagging to estimate the similarity between loans. Further more, to adapt to the loan temporality, RWH is dynamically established with temporal loans and lenders via a sliding window. Finally, we systematically evaluate our method on large-scale real-world datasets. The experimental results clearly demonstrate the effectiveness and robustness of our solutions.

Tracking the Dynamics in Crowdfunding

Crowdfunding is an emerging Internet fundraising mechanism by raising monetary contributions from the crowd for projects or ventures. In these platforms, the dynamics, i.e., daily funding amount on campaigns and perks (backing options with rewards), are the most concerned issue for creators, backers and platforms. However, tracking the dynamics in crowdfunding is very challenging and still under-explored. To that end, in this paper, we present a focused study on this important problem. A special goal is to forecast the funding amount for a given campaign and its perks in the future days. Specifically, we formalize the dynamics in crowdfunding as a hierarchical time series, i.e., campaign level and perk level. Specific to each level, we develop a special regression by modeling the decision making process of the crowd (visitors and backing probability) and exploring various factors that impact the decision; on this basis, an enhanced switching regression is proposed at each level to address the heterogeneity of funding sequences. Further, we employ a revision matrix to combine the two-level base forecasts for the final forecasting. We conduct extensive experiments on a real-world crowdfunding data collected from Indiegogo.com. The experimental results clearly demonstrate the effectiveness of our approaches on tracking the dynamics in crowdfunding.

Patent Litigation Prediction: A Convolutional Tensor Factorization Approach

Patent litigation is an expensive legal process faced by many companies. To reduce the cost of patent litigation, one effective approach is proactive management based on predictive analysis. However, automatic prediction of patent litigation is still an open problem due to the complexity of lawsuits. In this paper, we propose a data-driven framework, Convolutional Tensor Factorization (CTF), to identify the patents that may cause litigations between two companies. Specifically, CTF is a hybrid modeling approach, where the content features from the patents are represented by the Network embedding-combined Convolutional Neural Network (NCNN) and the lawsuit records of companies are summarized in a tensor, respectively. Then, CTF integrates NCNN and tensor factorization to systematically exploit both content information and collaborative information from large amount of data. Finally, the risky patents will be returned by a learning to rank strategy. Extensive experimental results on real-world data demonstrate the effectiveness of our framework.

Enhancing Campaign Design in Crowdfunding: A Product Supply Optimization Perspective

Crowdfunding is an emerging Internet application for creators designing campaigns (projects) to collect funds from public investors. Usually, the limited budget of the creator is manually divided into several perks (reward options), that should fit various market demand and further bring different monetary contributions for the campaign. Therefore, it is very challenging for each creator to design an effective campaign. To this end, in this paper, we aim to enhance the funding performance of the newly proposed campaigns, with a focus on optimizing the product supply of perks. Specifically, given the expected budget and the perks of a campaign, we propose a novel solution to automatically recommend the optimal product supply to every perk for balancing the expected return of this campaign against the risk. Along this line, we define it as a constrained portfolio selection problem, where the risk of each campaign is measured by a multitask learning method. Finally, experimental results on the real-world crowdfunding data clearly prove that the optimized product supply can help improve the campaign performance significantly, and meanwhile, our multi-task learning method could more precisely estimate the risk of each campaign.

Group Preference Aggregation: A Nash Equilibrium Approach

Group-oriented services such as group recommendations aim to provide services for a group of users. For these applications, how to aggregate the preferences of different group members is the toughest yet most important problem. Inspired by game theory, in this paper, we propose to explore the idea of Nash equilibrium to simulate the selections of members in a group by a game process. Along this line, we first compute the preferences (group-dependent optimal selections) of each individual member in a given group scene, i.e., an equilibrium solution of this group, with the help of two pruning approaches. Then, to get the aggregated unitary preference of each group from all group members, we design a matrix factorization-based method which aggregates the preferences in latent space and estimates the final group preference in rating space. After obtaining the group preference, group-oriented services (e.g., group recommendation) can be directly provided. Finally, we construct extensive experiments on two real-world data sets from multiple aspects. The results clearly demonstrate the effectiveness of our method.

Exploring the Procrastination of College Students: A Data-Driven Behavioral Perspective

Procrastination refers to the practice of putting off impending tasks due to the habitual carelessness or laziness. The understanding of procrastination plays an important role in educational psychology, which can help track and evaluate the comprehensive quality of students. However, traditional methods for procrastination analysis largely rely on the knowledge and experiences from domain experts. Fortunately, with the rapid development of college information systems, a large amount of student behavior records are captured, which enables us to analyze the behaviors of students in a quantitative way. To this end, in this paper, we provide a data-driven study from a behavioral perspective to understand the procrastination of college students. Specifically, we propose an unsupervised approach to quantitatively estimate the procrastination level of students by the analysis of their borrowing records in library. Along this line, we first propose a naive Reading-Procrastination (naive RP) model, which considers the behavioral similarity between students for procrastination discovery. Furthermore, to improve the discovery performance, we develop a dynamic Reading-Procrastination (dynamic RP) model by integrating more comprehensive characteristics of student behaviors, such as semester-awareness and month-regularity. Finally, we conduct extensive experiments on several real-world data sets. The experimental results clearly demonstrate the effectiveness of our approach, and verify several key findings from psychological fields.

A Structure-Enriched Neural Network for network embedding

Abstract Recent years have witnessed the importance of network embedding in many fields, as well as increased attention in academia. Although a number of algorithms have been proposed in this area, most existing models which only utilize the structure topology information of networks often suffer performance losses because of their insufficiency with regard to selecting structure similar patterns, handling noise data, and/or capturing non-linear or high-order structure information. To address these challenges, in this paper, we present a novel S tructure- E nriched N eural N etwork (SENN) for network embedding. Specifically, SENN can not only capture the complex structure similar patterns observed in networks by introducing direction adjustment parameters of the transition probability, but also introduce a stacked denoise autoencoder to perform the dimension reduction for each order matrix independently. Therefore, SENN can preserve more useful structure information and make the embeddings more robust. Moreover, SENN can effectively integrate the multi-order structure information by the combining layer with attention mechanism. Finally, to compare with other state-of-the-art methods, we conduct extensive experiments with both synthetic and real-world datasets on various tasks (e.g.,node classification, visualization). The experimental results clearly demonstrate the effectiveness of our proposed model for network embedding.