ﺳﯿﺴﺘﻢﻫﺎي ﺗﻮﺻﯿﻪ ﮔﺮ ﮔﺮدﺷﮕﺮي ﺳﻔﺮ ﺑﺮ اﺳﺎس اﻟﮕﻮرﯾﺘﻢ ﺧﻔﺎش و ﻓﯿﻠﺘﺮﯾﻨﮓ ﺗﺮﮐﯿﺒﯽ

Travel Tourism Recommendation Systems Based on Bat Algorithm and Hybrid Filtering

ﺣﺠﻢ ﺑﺴﯿﺎر و روﺑﻪ رﺷﺪ اﻃﻼﻋﺎت ﺑﺮ روي اﯾﻨﺘﺮﻧﺖ، ﻓﺮآﯾﻨﺪ ﺗﺼﻤﯿﻢﮔﯿﺮي و اﻧﺘﺨﺎب اﻃﻼﻋﺎت، داده ﯾﺎ ﮐﺎﻻﻫﺎي ﻣﻮردﻧﯿﺎز را، ﺑﺮاي ﺑﺴﯿﺎري از ﮐﺎرﺑﺮان وب دﺷﻮار ﮐﺮده اﺳﺖ. ﺳﺎﻣﺎﻧﻪﻫﺎي ﭘﯿﺸﻨﻬﺎددﻫﻨﺪه )ﺗﻮﺻﯿﻪ ﮔﺮ( ، ﺑﺎﻫﺪف رﻓﻊ اﯾﻦ ﭼﺎﻟﺶ ﺑﻪ وﺟﻮد آﻣﺪهاﻧﺪ و ﺗﻼش ﻣﯽﮐﻨﻨﺪ ﺗﺎ از ﻣﯿﺎن ﺣﺠﻢ ﻋﻈﯿﻢ اﻃﻼﻋﺎت، اﻃﻼﻋﺎت ﺧﺎص و ﻣﻔﯿﺪ را ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﻋﻼﻗﻪ و ﺳﻠﯿﻘﻪ ﮐﺎرﺑﺮ و ﺗﺠﺮﺑﯿﺎت ﮐﺎرﺑﺮان ﮔﺬﺷﺘﻪ ﺑﻪ وي ﭘﯿﺸﻨﻬﺎد دﻫﻨﺪ. ﺗﺎﮐﻨﻮن ﺳﺎﻣﺎﻧﻪﻫﺎي ﭘﯿﺸﻨﻬﺎددﻫﻨﺪه زﯾﺎدي در زﻣﯿﻨﻪﻫﺎي ﮐﺎرﺑﺮدي ﻣﺘﻨﻮع ازﺟﻤﻠﻪ ﻓﯿﻠﻢ، ﻣﻮﺳﯿﻘﯽ، ﮐﺘﺎب و ... اﯾﺠﺎدﺷﺪهاﻧﺪ. اﻧﺘﺨﺎب ﯾﮏ ﺳﻔﺮ ﻣﻨﺎﺳﺐ، ﭘﯿﺸﻨﻬﺎد ﻫﺘﻞ و ... ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﺑﻮدﺟﻪي ﻓﺮد، ﻣﻌﻤﻮﻻً ﺳﺨﺘﯽﻫﺎ و ﻧﮕﺮاﻧﯽﻫﺎي زﯾﺎدي را ﺑﺮاي ﮐﺎرﺑﺮان ﺑﻪ ﻫﻤﺮاه دارد و ﻋﻤﻮﻣﺎً ﺑﺎ ﺻﺮف زﻣﺎن و اﻧﺮژي زﯾﺎدي اﻧﺠﺎم ﻣﯽﮔﯿﺮد. ﻟﺬا در اﯾﻦ ﻣﻘﺎﻟﻪ ﯾﮏ ﺳﯿﺴﺘﻢ ﭘﯿﺸﻨﻬﺎددﻫﻨﺪه ﺳﻔﺮ و ﻫﺘﻞ اراﺋﻪ ﻣﯽﺷﻮد ﮐﻪ از ﺗﺮﮐﯿﺐ روش ﻓﯿﻠﺘﺮﻫﺎي ﻣﺨﺘﻠﻒ ﺳﺎﺧﺘﻪﺷﺪه اﺳﺖ ﺗﺎ دﻗﺖ آن دوﭼﻨﺪان ﺷﻮد. اﯾﻦ ﺳﯿﺴﺘﻢ ﺑﺮاي اراﺋﻪ ﭘﯿﺸﻨﻬﺎدﻫﺎي ﻧﻬﺎﯾﯽ ﺧﻮد، ﺳﻼﯾﻖ ﮐﺎرﺑﺮ ﺟﺎري، ﮐﯿﻔﯿﺖ ﻣﺠﻤﻮﻋﻪﻫﺎي ﺧﺪﻣﺎت دﻫﻨﺪه و ﺗﺠﺮﺑﯿﺎت ﮔﺬﺷﺘﻪ ﮐﺎرﺑﺮان ﻣﺸﺎﺑﻪ ﺑﺎ ﮐﺎرﺑﺮ ﺟﺎري را ﻣﺪﻧﻈﺮ ﻗﺮار داده و ﺑﺪﯾﻦ ﺗﺮﺗﯿﺐ ﻋﻼوه ﺑﺮ اراﺋﻪ ﭘﯿﺸﻨﻬﺎدﻫﺎي دﻗﯿﻖﺗﺮ، ﻣﺸﮑﻞ ﺷﺮوع ﺳﺮد را ﮐﻪ ﻣﻌﻤﻮﻻً ﺑﺮاي ﮐﺎرﺑﺮان ﺟﺪﯾﺪ ﺑﺮوز ﻣﯽﮐﻨﺪ ﮐﻪ در ﺳﯿﺴﺘﻢ ﺛﺒﺖﻧﺎم ﻣﯽﮐﻨﻨﺪ و ﺳﯿﺴﺘﻢ ﻫﯿﭻ اﻃﻼﻋﺎﺗﯽ از ﻧﻈﺮات ﯾﺎ ﻋﻼﯾﻖ ﮐﺎرﺑﺮ ﻧﺪارد، ﻧﯿﺰ ﺑﺮﻃﺮف ﻣﯽﻧﻤﺎﯾﺪ. در ﭼﻨﯿﻦ ﺷﺮاﯾﻄﯽ، ﺳﺎﻣﺎﻧﻪﻫﺎ ﻣﻌﻤﻮﻻً از ﯾﺎدﮔﯿﺮي ﻓﻌﺎل ﯾﺎ اﺳﺘﻔﺎده از وﯾﮋﮔﯽﻫﺎي ﺷﺨﺼﯿﺘﯽ ﮐﺎرﺑﺮ، ﺑﺮاي ﺣﻞ ﻣﺸﮑﻞ اﺳﺘﻔﺎده ﻣﯽﮐﻨﻨﺪ.

The growing amount of information on the internet has made it difficult for many web users to make the decision-making and selection of information, data or goods. Recommended systems are designed to address this challenge and try to offer specific and useful information with respect to user tastes and past user experiences. So far, many offering systems have been developed in a variety of applications including movies, music, books, hotels etc. Choosing the right trip, the hotel proposal and so on, with regard to the individual's budget usually have a lot of difficulties and concerns for users and generally takes a lot of time and energy. In this paper, a travel and hotel recommendation system is developed which is constructed from combination of different filtering methods to maximize accuracy. The system is considering the current user's preferences, the quality of the service packages and past experiences of the same users with the current user in order to providing more accurate suggestions. It also eliminates the cold start problem.