Smart object recommendation based on topic learning and joint features in the social internet of things

With the extensive integration of the Internet,social networks and the internet of things,the social internet of things has increasingly become a significant research issue.In the social internet of things application scenario,one of the greatest challenges is how to accurately recommend or match smart objects for users with massive resources.Although a variety of recommendation algorithms have been employed in this field,they ignore the massive text resources in the social internet of things,which can effectively improve the effect of recommendation.In this paper,a smart object recommendation approach named object recommendation based on topic learning and joint features is proposed.The proposed approach extracts and calculates topics and service relevant features of texts related to smart objects and introduces the“thing-thing”relationship information in the internet of things to improve the effect of recommendation.Experiments show that the proposed approach enables higher accuracy compared to the existing recommendation methods.

An Intelligent Recommendation System for Real Estate Commodity

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.