Most real estate agents develop new objects by visiting unfamiliar clients, distributing leaflets, or browsing other real estate trading website platforms,whereas consumers often rely on websites to search and compare...Most real estate agents develop new objects by visiting unfamiliar clients, distributing leaflets, or browsing other real estate trading website platforms,whereas consumers often rely on websites to search and compare prices when purchasing real property. In addition to being time consuming, this search processrenders it difficult for agents and consumers to understand the status changes ofobjects. In this study, Python is used to write web crawler and image recognitionprograms to capture object information from the web pages of real estate agents;perform data screening, arranging, and cleaning;compare the text of real estateobject information;as well as integrate and use the convolutional neural networkof a deep learning algorithm to implement image recognition. In this study, dataare acquired from two business-to-consumer real estate agency networks, i.e., theSinyi real estate agent and the Yungching real estate agent, and one consumer-toconsumer real estate agency platform, i.e., the, FiveNineOne real estate agent. Theresults indicate that text mining can reveal the similarities and differences betweenthe objects, list the number of days that the object has been available for sale onthe website, and provide the price fluctuations and fluctuation times during thesales period. In addition, 213,325 object amplification images are used as a database for training using deep learning algorithms, and the maximum image recognition accuracy achieved is 95%. The dynamic recommendation system for realestate objects constructed by combining text mining and image recognition systems enables developers in the real estate industry to understand the differencesbetween their commodities and other businesses in approximately 2 min, as wellas rapidly determine developable objects via comparison results provided by thesystem. Meanwhile, consumers require less time in searching and comparingprices after they have understood the commodity dynamic information, therebyallowing them to use the most efficient approach to purchase real estate objectsof their interest.展开更多
The rapid expansion of Web APIs presents developers with significant challenges in selecting optimal API compositions.To address this issue,keyword-based API composition recommendation techniques have been proposed.Ho...The rapid expansion of Web APIs presents developers with significant challenges in selecting optimal API compositions.To address this issue,keyword-based API composition recommendation techniques have been proposed.However,these methods often suffer from popularity bias due to the influence of historical datasets and recommendation models.This bias leads to the disproportionate recommendation of popular APIs over less popular ones,potentially causing the Matthew effect and impeding the balanced development of the API ecosystem.Although several studies have identified and attempted to mitigate popularity bias,they have largely relied on static analysis without accounting for the dynamic nature of API recommendations.In this paper,we introduce a dynamic simulation framework for API composition recommendations,designed to explore the evolution of popularity bias within recommendation results,and propose a debiasing method for dynamic recommendations by combining the enhanced API correlation graph with the Determinantal Point Process(DPP)method.Finally,extensive experiments on real datasets show that the algorithm effectively alleviates the popularity bias problem while guaranteeing high recommendation accuracy.展开更多
文摘Most real estate agents develop new objects by visiting unfamiliar clients, distributing leaflets, or browsing other real estate trading website platforms,whereas consumers often rely on websites to search and compare prices when purchasing real property. In addition to being time consuming, this search processrenders it difficult for agents and consumers to understand the status changes ofobjects. In this study, Python is used to write web crawler and image recognitionprograms to capture object information from the web pages of real estate agents;perform data screening, arranging, and cleaning;compare the text of real estateobject information;as well as integrate and use the convolutional neural networkof a deep learning algorithm to implement image recognition. In this study, dataare acquired from two business-to-consumer real estate agency networks, i.e., theSinyi real estate agent and the Yungching real estate agent, and one consumer-toconsumer real estate agency platform, i.e., the, FiveNineOne real estate agent. Theresults indicate that text mining can reveal the similarities and differences betweenthe objects, list the number of days that the object has been available for sale onthe website, and provide the price fluctuations and fluctuation times during thesales period. In addition, 213,325 object amplification images are used as a database for training using deep learning algorithms, and the maximum image recognition accuracy achieved is 95%. The dynamic recommendation system for realestate objects constructed by combining text mining and image recognition systems enables developers in the real estate industry to understand the differencesbetween their commodities and other businesses in approximately 2 min, as wellas rapidly determine developable objects via comparison results provided by thesystem. Meanwhile, consumers require less time in searching and comparingprices after they have understood the commodity dynamic information, therebyallowing them to use the most efficient approach to purchase real estate objectsof their interest.
基金supported by the Natural Science Foundation of Shandong Province(No.ZR2024MA004)the Teaching Reform Project of Qufu Normal University(No.SJG202227).
文摘The rapid expansion of Web APIs presents developers with significant challenges in selecting optimal API compositions.To address this issue,keyword-based API composition recommendation techniques have been proposed.However,these methods often suffer from popularity bias due to the influence of historical datasets and recommendation models.This bias leads to the disproportionate recommendation of popular APIs over less popular ones,potentially causing the Matthew effect and impeding the balanced development of the API ecosystem.Although several studies have identified and attempted to mitigate popularity bias,they have largely relied on static analysis without accounting for the dynamic nature of API recommendations.In this paper,we introduce a dynamic simulation framework for API composition recommendations,designed to explore the evolution of popularity bias within recommendation results,and propose a debiasing method for dynamic recommendations by combining the enhanced API correlation graph with the Determinantal Point Process(DPP)method.Finally,extensive experiments on real datasets show that the algorithm effectively alleviates the popularity bias problem while guaranteeing high recommendation accuracy.
