本文選題：推薦系統(tǒng)　切入點(diǎn)：就業(yè)推薦　出處：《山東師范大學(xué)》2017年碩士論文

【摘要】：就業(yè)推薦系統(tǒng)對(duì)于解決就業(yè)問(wèn)題具有良好的效果,因此受到國(guó)內(nèi)外學(xué)者廣泛關(guān)注,取得了豐富的成果。但是,就業(yè)推薦領(lǐng)域中,仍然存在以下不足之處有待進(jìn)一步完善:第一,推薦結(jié)果過(guò)于單一,用戶視野被局限。第二,熱門職位被推薦給過(guò)多的求職者,降低了求職成功幾率。第三,冷門職位得不到有效推薦,損害了招聘企業(yè)的利益。本文以就業(yè)推薦多樣性優(yōu)化為主要目標(biāo),針對(duì)上述問(wèn)題進(jìn)行了深入的研究。本文主要?jiǎng)?chuàng)新點(diǎn)及貢獻(xiàn)如下:(1)針對(duì)推薦系統(tǒng)中的個(gè)體多樣性問(wèn)題,提出一種基于聚類的個(gè)體多樣性優(yōu)化推薦算法。本文針對(duì)傳統(tǒng)就業(yè)推薦算法缺少對(duì)個(gè)體多樣性的考慮,推薦結(jié)果過(guò)于單一,提出一種基于聚類的個(gè)體多樣性優(yōu)化推薦算法。首先,算法針對(duì)系統(tǒng)中項(xiàng)目差異度進(jìn)行計(jì)算,充分考慮了項(xiàng)目屬性值間的差異;其次,基于項(xiàng)目差異度采用k-means聚類算法對(duì)系統(tǒng)中項(xiàng)目進(jìn)行聚類;然后,基于現(xiàn)有推薦算法獲得預(yù)測(cè)評(píng)分矩陣,設(shè)置評(píng)分閾值,篩選預(yù)測(cè)評(píng)分大于閾值的項(xiàng)目構(gòu)建用戶候選推薦列表;最后,結(jié)合項(xiàng)目聚類信息從用戶候選列表中獲得一組多樣性好的項(xiàng)目推薦給用戶。實(shí)驗(yàn)結(jié)果表明,對(duì)于用戶個(gè)體而言,該算法在保證推薦準(zhǔn)確率的同時(shí),能有效提高推薦結(jié)果的多樣性。通過(guò)將算法應(yīng)用于就業(yè)推薦原型系統(tǒng)表明,基于聚類的個(gè)體多樣性優(yōu)化推薦算法,可有效提高就業(yè)推薦的個(gè)體多樣性與用戶滿意度。(2)針對(duì)推薦系統(tǒng)中的總體多樣性問(wèn)題,提出一種基于二分圖網(wǎng)絡(luò)的總體多樣性優(yōu)化推薦算法。本文針對(duì)傳統(tǒng)就業(yè)推薦算法缺少對(duì)總體多樣性的考慮,造成系統(tǒng)“馬太效應(yīng)”日益嚴(yán)重,“長(zhǎng)尾”職位數(shù)量增多的現(xiàn)象,提出一種基于二分圖網(wǎng)絡(luò)的總體多樣性優(yōu)化推薦算法。首先,算法基于現(xiàn)有推薦算法獲得預(yù)測(cè)評(píng)分矩陣,設(shè)置評(píng)分閾值,篩選預(yù)測(cè)評(píng)分大于閾值的項(xiàng)目構(gòu)建用戶候選推薦列表。其次,基于用戶候選推薦列表構(gòu)建推薦二分圖。最后,基于構(gòu)建的推薦二分圖,采用置換增廣路中匹配邊與非匹配邊方法,提高推薦總體多樣性。實(shí)驗(yàn)結(jié)果表明,對(duì)于系統(tǒng)整體而言,該算法在保證推薦準(zhǔn)確率的同時(shí),能有效提高推薦總體多樣性。應(yīng)用于就業(yè)推薦領(lǐng)域的基于二分圖網(wǎng)絡(luò)的總體多樣性優(yōu)化推薦算法,可有效提高就業(yè)推薦的總體多樣性與用戶滿意度。(3)基于上述兩種多樣性優(yōu)化策略,實(shí)現(xiàn)了就業(yè)推薦原型系統(tǒng)。
[Abstract]:The employment recommendation system has a good effect on solving the employment problem, so it has received extensive attention from scholars at home and abroad, and has made a lot of achievements. However, in the field of employment recommendation, there are still the following shortcomings to be further improved: first, Recommendation results are too single, user horizons are limited. Second, hot jobs are recommended to too many job seekers, reducing their chances of success. Third, bad jobs are not recommended effectively. This paper focuses on the optimization of the diversity of employment recommendation, and makes a deep research on the above problems. The main innovation and contribution of this paper are as follows: 1) aiming at the individual diversity problem in the recommendation system. An optimal recommendation algorithm for individual diversity based on clustering is proposed in this paper. In view of the lack of consideration of individual diversity in the traditional employment recommendation algorithm, the recommendation result is too single. This paper proposes a clustering based recommendation algorithm for individual diversity optimization. Firstly, the algorithm calculates the item difference degree in the system, and fully considers the difference between item attribute values. K-means clustering algorithm is used to cluster the items in the system based on the item difference degree, and then, based on the existing recommendation algorithm, the prediction scoring matrix is obtained, the scoring threshold is set up, and the user candidate recommendation list is constructed by screening the items whose prediction score is greater than the threshold value. Finally, a group of items with good diversity is obtained from the user candidate list by combining the item clustering information. The experimental results show that the proposed algorithm ensures the accuracy of the recommendation for the user at the same time. It can effectively improve the diversity of recommendation results. By applying the algorithm to the employment recommendation prototype system, it is shown that the clustering based individual diversity optimization recommendation algorithm, It can effectively improve the individual diversity of employment recommendation and user satisfaction. This paper presents an optimal recommendation algorithm for population diversity based on bipartite graph network. In view of the lack of consideration of the overall diversity in the traditional employment recommendation algorithm, the "Matthew effect" of the system is becoming more and more serious, and the number of "long tail" posts is increasing. In this paper, a general diversity optimization recommendation algorithm based on bipartite graph network is proposed. Firstly, based on the existing recommendation algorithms, the prediction score matrix is obtained and the threshold is set. Second, build the recommended dichotomy based on the user candidate recommendation list. Finally, based on the constructed recommendation dichotomy, The method of matching edge and mismatch edge in permutation augmented path is used to improve the diversity of recommendation population. The experimental results show that the proposed algorithm not only guarantees the accuracy of recommendation, but also ensures the accuracy of recommendation for the whole system. It can effectively improve the overall diversity of recommendation. The overall diversity optimization recommendation algorithm based on bipartite graph network is applied in the field of employment recommendation. It can effectively improve the overall diversity of employment recommendation and user satisfaction. 3) based on the above two kinds of diversity optimization strategies, the prototype system of employment recommendation is implemented.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

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