Potential customers of E-commerce garment businesses purchase clothes based on their personal choice as well as contemporary perception about fashion trends which is highly sublime in nature. This, coupled with the influential influx of cinema, social media and the Internet has led to rapid change in dressing preferences. E-commerce sites have a huge inventory categorized based on different attributes like colour, fabric, fit, product type and vendor. Hence, it is a tedious task for the user to find products coherent with the latest trend and appropriate according to their personal taste and global trends. The recommendation engines of these websites gives recommendations based only on a linear similarity of products, not comprehensively taking into account latest trends in fashion backed by fashion specialists, user preference or wardrobe collections. We propose an Adaptive Commodity Suggestion System called ”Match-BOT” which takes into account the trends obtained through insights provided by Google Trends, individual preferences, and similarity between user preferences. The user preferences are extracted by analyzing user purchase patterns and thus obtaining categorised product attribute preferences in the form of weights. This is an application of Content Based Learning The global trend obtained from Google Trends is then overlapped with the user preference and a user specific suggestion list is generated. The similarity between two user is calculated using Collaborative Filtering and then taken into account by including cross-user suggestions based on the degree of correlation. The output of this process would be in the form of list of suggestions of products which have been customized to society's understanding of fashion and predilections of the user and constantly updated to reflect dynamic global trends. This would result in an unparalleled browsing experience for the user and an efficient sales and successive inventory management systems.
{"title":"Adaptive Commodity Suggestion System — Match-BOT","authors":"Shreyaan Kaushal, Jatin Agrawal, Taranveer Singh, Manav Gulati","doi":"10.1109/ICDIPC.2015.7323046","DOIUrl":"https://doi.org/10.1109/ICDIPC.2015.7323046","url":null,"abstract":"Potential customers of E-commerce garment businesses purchase clothes based on their personal choice as well as contemporary perception about fashion trends which is highly sublime in nature. This, coupled with the influential influx of cinema, social media and the Internet has led to rapid change in dressing preferences. E-commerce sites have a huge inventory categorized based on different attributes like colour, fabric, fit, product type and vendor. Hence, it is a tedious task for the user to find products coherent with the latest trend and appropriate according to their personal taste and global trends. The recommendation engines of these websites gives recommendations based only on a linear similarity of products, not comprehensively taking into account latest trends in fashion backed by fashion specialists, user preference or wardrobe collections. We propose an Adaptive Commodity Suggestion System called ”Match-BOT” which takes into account the trends obtained through insights provided by Google Trends, individual preferences, and similarity between user preferences. The user preferences are extracted by analyzing user purchase patterns and thus obtaining categorised product attribute preferences in the form of weights. This is an application of Content Based Learning The global trend obtained from Google Trends is then overlapped with the user preference and a user specific suggestion list is generated. The similarity between two user is calculated using Collaborative Filtering and then taken into account by including cross-user suggestions based on the degree of correlation. The output of this process would be in the form of list of suggestions of products which have been customized to society's understanding of fashion and predilections of the user and constantly updated to reflect dynamic global trends. This would result in an unparalleled browsing experience for the user and an efficient sales and successive inventory management systems.","PeriodicalId":339685,"journal":{"name":"2015 Fifth International Conference on Digital Information Processing and Communications (ICDIPC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128791236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-01DOI: 10.1109/ICDIPC.2015.7323029
B. Bučko, Vladimír Hanušniak, Michal Joštiak, Katarína Zábovská
Knowledge can be defined as a fact or state which is based on gathered information, experiences and association. Storing and gathering knowledge is an actual issue in artificial intelligence, health care and manufacturing. Ontologies use mechanism that offers inferring of the new facts out of explicitly given ones. We have used this feature for solving a logic riddle. The speed of inferring facts/knowledge is crucial. This paper presents several experiments with various versions of ontological model and compares inferring speed of the chosen riddle within these models.
{"title":"Inferring over functionally equivalent ontologies","authors":"B. Bučko, Vladimír Hanušniak, Michal Joštiak, Katarína Zábovská","doi":"10.1109/ICDIPC.2015.7323029","DOIUrl":"https://doi.org/10.1109/ICDIPC.2015.7323029","url":null,"abstract":"Knowledge can be defined as a fact or state which is based on gathered information, experiences and association. Storing and gathering knowledge is an actual issue in artificial intelligence, health care and manufacturing. Ontologies use mechanism that offers inferring of the new facts out of explicitly given ones. We have used this feature for solving a logic riddle. The speed of inferring facts/knowledge is crucial. This paper presents several experiments with various versions of ontological model and compares inferring speed of the chosen riddle within these models.","PeriodicalId":339685,"journal":{"name":"2015 Fifth International Conference on Digital Information Processing and Communications (ICDIPC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123500135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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