Status quo and future trends of 2015children’s publications released by the Shanghai Press and Publication shows that in the past decade,the domestic children’s book market is developing rapidly with an average annu...Status quo and future trends of 2015children’s publications released by the Shanghai Press and Publication shows that in the past decade,the domestic children’s book market is developing rapidly with an average annual growth of 10%.Children’s books are seeing an increasing ratio with a market share of over 40%.展开更多
Toyamara Da Vera Cruz is all ears when Peng Yansen, her teacher, glves instructions. Although she has extensive experience in farming herself. coming from a long line of livestock breeders, Cruz is now learning a new ...Toyamara Da Vera Cruz is all ears when Peng Yansen, her teacher, glves instructions. Although she has extensive experience in farming herself. coming from a long line of livestock breeders, Cruz is now learning a new skill from Peng.展开更多
Popularity plays a significant role in the recommendation system. Traditional popularity is only defined as a static ratio or metric (e.g., a ratio of users who have rated the item and the box office of a movie) regar...Popularity plays a significant role in the recommendation system. Traditional popularity is only defined as a static ratio or metric (e.g., a ratio of users who have rated the item and the box office of a movie) regardless of the previous trends of this ratio or metric and the attribute diversity of items. To solve this problem and reach accurate popularity, we creatively propose to extract the popularity of an item according to the Proportional Integral Differential (PID) idea. Specifically, Integral (I) integrates a physical quantity over a time window, which agrees with the fact that determining the attributes of items also requires a long-term observation. The Differential (D) emphasizes an incremental change of a physical quantity over time, which coincidentally caters to a trend. Moreover, in the Session-Based Recommendation (SBR) community, many methods extract session interests without considering the impact of popularity on interest, leading to suboptimal recommendation results. To further improve recommendation performance, we propose a novel strategy that leverages popularity to enhance the session interest (popularity-aware interest). The proposed popularity by PID is further used to construct the popularity-aware interest, which consistently improves the recommendation performance of the main models in the SBR community. For STAMP, SRGNN, GCSAN, and TAGNN, on Yoochoose1/64, the metric P@20 is relatively improved by 0.93%, 1.84%, 2.02%, and 2.53%, respectively, and MRR@20 is relatively improved by 3.74%, 1.23%, 2.72%, and 3.48%, respectively. On Movieslen-1m, the relative improvements of P@20 are 7.41%, 15.52%, 8.20%, and 20.12%, respectively, and that of MRR@20 are 2.34%, 12.41%, 20.34%, and 19.21%, respectively.展开更多
文摘Status quo and future trends of 2015children’s publications released by the Shanghai Press and Publication shows that in the past decade,the domestic children’s book market is developing rapidly with an average annual growth of 10%.Children’s books are seeing an increasing ratio with a market share of over 40%.
文摘Toyamara Da Vera Cruz is all ears when Peng Yansen, her teacher, glves instructions. Although she has extensive experience in farming herself. coming from a long line of livestock breeders, Cruz is now learning a new skill from Peng.
基金supported by the National Natural Science Foundation of China(No.62276278)Guangdong Basic and Applied Basic Research Foundation(No.2022A1515110006).
文摘Popularity plays a significant role in the recommendation system. Traditional popularity is only defined as a static ratio or metric (e.g., a ratio of users who have rated the item and the box office of a movie) regardless of the previous trends of this ratio or metric and the attribute diversity of items. To solve this problem and reach accurate popularity, we creatively propose to extract the popularity of an item according to the Proportional Integral Differential (PID) idea. Specifically, Integral (I) integrates a physical quantity over a time window, which agrees with the fact that determining the attributes of items also requires a long-term observation. The Differential (D) emphasizes an incremental change of a physical quantity over time, which coincidentally caters to a trend. Moreover, in the Session-Based Recommendation (SBR) community, many methods extract session interests without considering the impact of popularity on interest, leading to suboptimal recommendation results. To further improve recommendation performance, we propose a novel strategy that leverages popularity to enhance the session interest (popularity-aware interest). The proposed popularity by PID is further used to construct the popularity-aware interest, which consistently improves the recommendation performance of the main models in the SBR community. For STAMP, SRGNN, GCSAN, and TAGNN, on Yoochoose1/64, the metric P@20 is relatively improved by 0.93%, 1.84%, 2.02%, and 2.53%, respectively, and MRR@20 is relatively improved by 3.74%, 1.23%, 2.72%, and 3.48%, respectively. On Movieslen-1m, the relative improvements of P@20 are 7.41%, 15.52%, 8.20%, and 20.12%, respectively, and that of MRR@20 are 2.34%, 12.41%, 20.34%, and 19.21%, respectively.
