Recommender System for Real Estate Management

Ginevičius, Tomas; Kaklauskas, Artūras; Kazokaitis, Paulius; Alchimovienė, Jurgita

doi:10.3846/btp.2011.26

Cited by 4 publications

(4 citation statements)

References 6 publications

(13 reference statements)

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“…It is generally considered as a challenging task because of the strict expectations from the information-seeker [50]. Fields of application for recommender systems in finance-related domains include stock investment [14], real estate investment [15], crowdfunding projects [3], and portfolio management [30], among others. As for venture investment, there have been relatively few scientific publications on the topic of recommendation.…”

Section: Recommender Systems In Investment Domainsmentioning

confidence: 99%

“…In several financial investment scenarios, including stock investment [14], real estates investment [15], crowdfunding project [3], and portfolio management [30], recommender systems have received increasing attention [50]. Less attention has been devoted to the design and applications of recommendation systems in the domain of venture investment, an emerging investment type that aims at offering seed funding to startup companies.…”

Section: Introductionmentioning

confidence: 99%

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Recommending investors for new startups by integrating network diffusion and investors’ domain preference

Zhang

Lü

2020

Information Sciences

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Over the past decade, many startups have sprung up, which create a huge demand for financial support from venture investors. However, due to the information asymmetry between investors and companies, the financing process is usually challenging and time-consuming, especially for the startups that have not yet obtained any investment. Because of this, effective data-driven techniques to automatically match startups with potentially relevant investors would be highly desirable. Here, we analyze 34, 469 valid investment events collected from www.itjuzi.com and consider the cold-start problem of recommending investors for new startups. We address this problem by constructing different tripartite network representations of the data where nodes represent investors, companies, and companies' domains. First, we find that investors have strong domain preferences when investing, which motivates us to introduce virtual links between investors and investment domains in the tripartite network construction. Our analysis of the recommendation performance of diffusion-based algorithms applied to various network representations indicates that prospective investors for new startups are effectively revealed by integrating network diffusion processes with investors' domain preference.Our main goal is to fill this gap by designing and validating recommendation system techniques to identify suitable investors for new startups. To this end, we analyze 34,469 investment events collected from www.itjuzi.com [1]. As we focus on startups that received no previous investments in the past, widely-studied recommendation techniques based on bipartite networks [49] are not applicable here since the new startups with no previous investors turn out to be isolated nodes in the investor-company bipartite network, making them unreachable by diffusion processes. Our problem can be classified as a cold-start problem [35]: When a new actor enters the system, there is insufficient past information to provide him/her with a recommendation [26].To overcome the cold-start problem, we resort to tagging techniques [16].On the www.itjuzi.com platform, each startup is required to provide several tags that define its business scope. We found that the investors have a strong preference toward a small number of tags -in particular, in the majority of cases, an investor tends to invest in startups that feature her favorite tag. Hence, motivated by this finding and the key role played by the industry field in investment decision making [10, 28], we use tags as a key piece of information to generate recommendations.By leveraging startups' tag information, we construct three different tripartite network representations of the investment system. The most natural tripartite representation is one where investors are connected to the startups they invested in, and startups are connected to their self-reported tags -we refer to this representation as the tagcompany-investor (TCI) representation. This representation is natural because it is directly based on the collected ...

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Section: Recommender Systems In Investment Domainsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recommending investors for new startups by integrating network diffusion and investors’ domain preference

Zhang

Lü

2020

Information Sciences

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“…Recommender System has improved over the years in terms of sophistication and connectivity to other systems due to the improved quality and quantity of available data to the Recommender System [23] [28]. The major goal of any recommender system as described by T., Ginevicius et al [21] is to maximize the "trust" property to increase the user's confidence in the systems generated recommendations. Users might not trust recommendations from a system because of the data origin.…”

Section: Introductionmentioning

confidence: 99%

A Model of Intelligent Recommender System with Explicit Feedback Mechanism for Performance Improvement

Awodele¹,

Temitope.²,

Adekunle³

et al. 2020

IJIRCST

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Recommender Systems are intelligent applications designed to assist the user in a decision-making process whereby user wants to choose one item amongst the potentially overwhelming set of alternative products or services. This work focused on using users' bank statements that explicitly shows inflow and outflow of funds. The dataset used is real and reliable because the use of non-reliable data in a recommender system causes users lack of trust in the system. However, the data collected were anonymized for privacy reasons. The recommender system was developed as a web application using Java programming language. Unlike other recommender systems, the graph-oriented database management system was used. In Google news, 38% of the total views are the result of recommendations; similarly, 60% of the rented movies from Netflix come from recommendations and more than that Amazon sales percentage due to recommendations are 35%. Successful integration of recommendation system by online companies like Amazon, eBay, Flipkart amongst others impelled the research community to avail similar benefits in Financial domain to recommend product and services [26]. Therefore, recommendation systems are considered an expedient factor in business nowadays. The aim of all recommender systems is to provide recommendation that will be favourably evaluated and accepted by its users.

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“… 5 41. Ginevičius et al [ 79 ] The text explores real estate management methods such as Nearest Neighbourhood and Collaborative Filtering, highlighting challenges in evaluating strengths and suggesting improvements. Common recommendation approaches include content-based and collaborative methods, with the desire for more flexible and less intrusive recommendations.…”

mentioning

confidence: 99%

Housing fuzzy recommender system: A systematic literature review

Muñoz,

Parraga-Alava,

Meza

et al. 2024

Heliyon

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Recommender System for Real Estate Management

Cited by 4 publications

References 6 publications

Recommending investors for new startups by integrating network diffusion and investors’ domain preference

Recommending investors for new startups by integrating network diffusion and investors’ domain preference

A Model of Intelligent Recommender System with Explicit Feedback Mechanism for Performance Improvement

Housing fuzzy recommender system: A systematic literature review

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