本文選題：旅游推薦 + 推薦系統(tǒng)��； 參考：《計算機研究與發(fā)展》2017年02期

【摘要】：雖然目前旅游者可以利用Web搜索引擎來選擇旅游景點,但往往難以獲得較好符合自身需要的旅游規(guī)劃.而旅游推薦系統(tǒng)是解決上述問題的有效方式.一個好的旅游推薦模型應具有個性化并能考慮用戶時間和費用的限制.調研表明,用戶在選擇旅游景點時,目的地與用戶常居地的距離常常是一個需要考慮的問題.因為旅行距離往往可以間接地反映了時間和費用的影響.于是,在貝葉斯模型和概率矩陣分解模型的基礎上,提出一個旅行距離敏感的旅游推薦模型(geographical probabilistic matrix factorization,GeoPMF).主要思想是基于每個用戶的旅游歷史,推算出一個最偏好的旅游距離,并作為一種權重,添加到傳統(tǒng)的基于概率矩陣分解的推薦模型中.在攜程網(wǎng)站的旅游數(shù)據(jù)集上的實驗表明,與基準方法相比,GeoPMF的RMSE(root mean square error)可以降低近10%;與傳統(tǒng)概率矩陣分解模型(PMF)相比,通過考慮距離因子,RMSE平均降幅近3.5%.
[Abstract]:At present, tourists can use Web search engine to choose tourist attractions, but it is often difficult to obtain a better tourism planning that meets their needs. Tourism recommendation system is an effective way to solve the above problems. A good travel recommendation model should be personalized and take into account user time and expense constraints. The research shows that the distance between the destination and the user's place of residence is often a problem to be considered when users choose tourist attractions. Because travel distance can often indirectly reflect the impact of time and expenses. On the basis of Bayesian model and probability matrix decomposition model, a travel recommendation model (geographical probabilistic matrix factorization _ GeoPMF) is proposed. The main idea is to calculate a preferred travel distance based on each user's travel history and add it to the traditional recommendation model based on probability matrix decomposition as a weight. The experiment on the travel data set of Ctrip station shows that compared with the reference method, the RMSE (root mean square error) of GeoPMF can be reduced by nearly 10%, and the average decrease of RMSE by taking into account the distance factor is about 3.5% compared with the traditional probability matrix decomposition model (PMF).
【作者單位】： 山東大學計算機科學與技術學院;
【基金】：國家自然科學基金項目(61272240,61672322) 山東省自然科學基金項目(ZR2012FM037) 微軟國際合作基金項目(FY14-RESTHEME-25)~~
【分類號】：TP391.3
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本文編號：2079410