WO2021139164A1 - 一种基于长短期兴趣的序列化推荐方法 - Google Patents
一种基于长短期兴趣的序列化推荐方法 Download PDFInfo
- Publication number
- WO2021139164A1 WO2021139164A1 PCT/CN2020/110549 CN2020110549W WO2021139164A1 WO 2021139164 A1 WO2021139164 A1 WO 2021139164A1 CN 2020110549 W CN2020110549 W CN 2020110549W WO 2021139164 A1 WO2021139164 A1 WO 2021139164A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- user
- term
- long
- short
- method based
- Prior art date
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0282—Rating or review of business operators or products
Definitions
- the present invention relates to the field of serialized recommendation and recommendation system based on deep learning, and in particular to a method for serialized product recommendation based on long- and short-term preferences.
- the near-recommendation system tries to recommend items that users want to buy or interact with in the future based on users' interests or preferences.
- a large number of user interactions such as browsing, clicking, collecting, shopping carts, purchases
- These logs containing sufficient information provide a data basis for researching user preferences and personalized recommendations.
- the existing recommender systems model the interaction between users and items can be summarized into two main ways.
- the first method is collaborative filtering (CF) based on matrix factorization to obtain user preferences.
- CF collaborative filtering
- These works focus on mining their static associations from the interactions between users and items, and these associations are represented by traditional collaborative filtering models.
- these works only consider the specific user-item relationship from a static view, ignore the evolution of user preferences implicit in serialized interactions, and fail to consider the impact of the evolution of user preferences on future purchases of items.
- the second method is to mine the relationship between users and items based on sequence patterns to make personalized recommendations.
- the user's stable long-term preference is the preference caused by personal habits for a long time;
- the short-term preference is the preference determined by the user's recent purchase.
- This type of work includes: modeling the interaction sequence between users and commodities according to the Markov chain model; modeling the interaction sequence between users and commodities according to the RNN model.
- the existing sequence model can predict the items that the user may purchase in the next purchase based on the sequence of interaction behaviors, it has the following two shortcomings: First, these methods focus on directly using the sequence of items to express the relationship between items. However, because different users pay attention to different aspects when purchasing the same product, the item vector that directly uses the similarity between items cannot directly represent the user’s preference. Second, the existing model ignores the user’s immediate interest. Different from the user's short-term preference, instant interest is an instant, specific demand when a user wants to purchase a product or a series of products.
- the present invention provides a method based on the user's stable long-term preference and dynamic instant interest aggregation to better perform personalized recommendation.
- the technical scheme of the present invention is:
- a serialized recommendation method based on long-term and short-term interests includes the following steps:
- S2 Process all review texts and question texts, select multiple words with the highest scores from the relevant texts of each product as the extracted features, describe the product through the collection of all features, and construct a feature representation matrix of the product;
- S3 Construct a vector representation of the user purchase sequence: obtain a vector representation of each user's purchase sequence according to the feature representation matrix of the product and the user's historical purchase sequence;
- S4 respectively express the user's long-term interest preference and short-term interest preference
- S5 Use the user's long-term interest preferences and short-term interest preferences to obtain user aggregate preferences through the Attention mechanism;
- S7 Use the cross-entropy loss function to learn the parameters of the model, and get the probability of each item being purchased after the question moment.
- the preprocessing in S1 includes: sorting the purchase data, comment data, and question data of each user in chronological order, and filtering users with low total purchases.
- the number of selected words with the highest score in the S2 is greater than or equal to 5.
- a serialized recommendation method based on long-term and short-term interests is used to process the comment text and the question text.
- a serialized recommendation method based on long-term and short-term interests uses the value of the bidirectional RNN hidden unit to represent the user's long-term preferences.
- the short-term interest preference uses the CoreNLP algorithm to process the user's question text at a certain moment, and obtains the scores of the features that the user pays more attention to in the question, and the short-term interest of the user Preference is expressed.
- the relationship between the aggregated preference and the target item in the S6 is determined by using a fully connected layer.
- the beneficial effects of the present invention are: the user's long-term preference can be modeled according to the historical interaction sequence between the user and the commodity through the recurrent neural network; the user's questions about the commodity can extract the user's immediate interest in the short-term; the long-term preference can be adjusted based on the attention mechanism Aggregate with instant interests to make personalized recommendations for users in the next moment. It can effectively solve the problem of inaccurate recommendation caused by the evolution of user preferences, and at the same time can effectively indicate the different degrees of dependence of different users on long-term preferences and instant interests.
- Figure 1 is a flow chart of a serialized recommendation method based on long-term and short-term interests of the present invention
- Figure 2 is a model diagram of a serialized recommendation method based on long- and short-term interests
- Figure 3 shows the change of the recommendation performance Recall and HR with the length of the recommendation list in a serialized recommendation method based on long-term and short-term interests
- Figure 4 shows a serialized recommendation method based on long-term and short-term interests to show the degree of dependence of different users on long-term preferences and instant interests.
- the present invention processes user purchase sequence data and user question data in the data set, and obtains serialized interaction data between the user and the product based on this, and extracts the user’s comments on the product to represent the characteristics of the product; next, recursion is used Recursive Neural Network (RNN) learns the user’s stable long-term preferences from the user’s historical purchase sequence data, and uses the questioning data to model the user’s instant interest; finally, for stable long-term preferences and dynamic instant interests, this article Attention mechanism is used to characterize the degree of dependence of different users on these two features.
- RNN Recursive Neural Network
- the preprocessing in the present invention includes: sorting the purchase data, comment data, and question data of each user in chronological order, and filtering users with low total purchases. To ensure the accuracy of the recommendation, this embodiment filters out users whose total number of purchases is less than 5 times.
- S2 Process all the comment text and question text.
- the present invention uses the TF-IDF method to process the comment text and question text.
- S3 Construct a vector representation of the user's purchase sequence.
- the vector representation of each user’s purchase sequence is obtained, which is expressed as:
- i j , f j and o j correspond to the input gate, forget gate and output gate of the GRU, respectively
- b j is the vector representation of the shopping basket at the current moment
- c j is the value of the GRU memory unit
- h j is the hidden state of step j; with Respectively represent the hidden unit value obtained by using two-way RNN, and concatenate them to obtain Therefore, the long-term preference of user u is expressed as:
- longP u average(h 1 , h 2 ,..., h tq );
- S5 Express the user's short-term interest preferences.
- the short-term interest preference modeling mainly uses the CoreNLP algorithm to process the user 's question text at t q , and obtains the score of the feature that the user pays more attention to in the question. Therefore, the short-term preference of the user u can be described in the form:
- S6 From the long-term interest preference obtained by S4 and the short-term interest preference obtained by S5, the user aggregated preference combining long-term and short-term interests can be obtained through the Attention mechanism.
- the aggregated preference is expressed as:
- Step 7 Use the cross-entropy loss function to learn the parameters of the model to obtain the probability of each item being purchased after the question time t q, which is described as:
- ⁇ represents the items observed in the historical purchase sequence
- ⁇ - represents a negative instance. All unobserved commodities can be regarded as a negative instance, or a negative sampling method can be adopted.
- the recommendation results are shown in Figures 3 and 4, predicting the products that the user may purchase at the next moment, and obtaining the prediction score vector, thereby recommending top-K products to the user.
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
一种基于长短期兴趣的序列化推荐方法,通过处理数据集中的用户购买序列数据以及用户提问数据,得到用户与商品的序列化交互数据,提取用户对商品的评论内容来表示商品的特征;接着使用递归神经网络从用户的历史购买序列数据中学习用户稳定的长期偏好,同时使用提问数据来建模用户的即时兴趣;最后,对于稳定的长期偏好和动态的即时兴趣,使用注意力(Attention)机制来刻画不同用户对这两个特征的依赖程度;能够有效解决由于用户偏好演化而导致的推荐不准确的问题,同时可以有效表示不同用户对于长期偏好与即时兴趣不同的依赖程度。
Description
本发明涉及序列化推荐和基于深度学习的推荐***领域,尤其涉及基于长短期偏好的序列化商品推荐的方法。
近推荐***作为现代电子商务网站的重要组成部分,试图根据用户的兴趣或偏好来推荐未来用户想要购买或交互的物品。随着电子商务机制的发展,大量的用户交互(如浏览、点击、收集、购物车、购买)被记录下来,其中隐藏着用户的消费模式。这些包含充分信息量的日志为研究用户的偏好以及个性化推荐提供了数据基础。
现有的推荐***对用户与物品之间交互的建模可以归纳为两种主要的方式。第一种方法是基于矩阵分解的协同过滤(CF)来获得用户偏好,这些工作侧重于从用户与物品的交互中挖掘其静态关联,这些关联由传统的协同过滤模型表示。然而,这些工作仅仅从静态视图考虑了用户-物品之间特定关系,忽略了序列化交互中隐含的用户偏好的演化,没有考虑到用户偏好的演化对未来购买物品的影响。
第二种方法是基于序列模式来挖掘用户与物品之间的关系从而进行个性化推荐。其中用户稳定的长期偏好是长期以来个人习惯导致的偏好;短期偏好是用户近期购买的物品决定的偏好。这种类型的工作包括:根据马尔科夫链模型建模用户与商品的交互序列;根据RNN模型建模用户与商品的交互序列。
现有的序列模型虽然可以基于交互行为序列预测用户下一次购 买时可能购买的项目,但是存在以下两点不足:第一,这些方法侧重于直接使用物品之间的序列性表示物品之间关系,但由于不同的用户在购买相同的产品时关注不同的方面,导致直接使用物品之间的相似性表示的物品向量无法直接代表用户的偏好,第二,现有的模型忽略了用户的即时兴趣,不同于用户的短期偏好,即时兴趣是当用户想要购买一个产品或一系列产品时,即时的、特定的需求。
发明内容
针对以上缺陷,本发明提供一种基于用户稳定的长期偏好以及动态的即时兴趣进行聚合可以更好的进行个性化推荐的方法。本发明的技术方案为:
一种基于长短期兴趣的序列化推荐方法,所述方法包括以下步骤:
S1:获取数据,对数据进行预处理;
S2:对所有的评论文本、提问文本进行处理,对每个商品的相关文本中选择得分最高的多个词作为提取特征,通过所有特征的集合来对商品进行描述,构建商品的特征表示矩阵;
S3:构建用户购买序列的向量表示:根据商品的特征表示矩阵以及用户的历史购买序列得到每个用户购买序列的向量表示;
S4:分别对用户的长期兴趣偏好和短期兴趣偏好进行表示;
S5:将用户的长期兴趣偏好和短期兴趣偏好通过Attention机制获得用户聚合偏好;
S6:通过确定聚合偏好和目标物品之间的关系,获得用户提问之 后与物品交互的概率;
S7:使用交叉熵损失函数来学习模型的参数,得到提问时刻后每个物品被购买的概率。
进一步的,一种基于长短期兴趣的序列化推荐方法,所述S1中预处理包括:将每个用户的购买数据、评论数据以及提问数据按照时间顺序排序、过滤总购买数低的用户。
进一步的,一种基于长短期兴趣的序列化推荐方法,所述S2中选择得分最高的多个词的个数为大于等于5个。
进一步的,一种基于长短期兴趣的序列化推荐方法,所述S2中,使用TF-IDF方法对评论文本、提问文本进行处理。
进一步的,一种基于长短期兴趣的序列化推荐方法,根据用户购买序列的向量表示,使用双向RNN隐藏单元的值对用户的长期偏好进行表示。
进一步的,一种基于长短期兴趣的序列化推荐方法,短期兴趣偏好使用CoreNLP算法对用户在某一时刻的提问文本进行处理,得到用户在提问中比较关注的特征的分数,对用户的短期兴趣偏好进行表示。
进一步的,一种基于长短期兴趣的序列化推荐方法,所述S6中的所述聚合偏好和目标物品之间的关系通过使用全连接层来确定。
本发明的有益效果为:通过递归神经网络可以根据用户与商品的历史交互序列建模用户的长期偏好;用户对于商品的提问可以提取出 用户短期内的即时兴趣;基于注意力机制可以将长期偏好与即时兴趣进行聚合,从而在下一时刻为用户进行个性化推荐。其能够有效解决由于用户偏好演化而导致的推荐不准确的问题,同时可以有效表示不同用户对于长期偏好与即时兴趣不同的依赖程度。
图1为本发明一种基于长短期兴趣的序列化推荐方法流程图;
图2为一种基于长短期兴趣的序列化推荐方法模型图;
图3为一种基于长短期兴趣的序列化推荐方法中表示推荐性能Recall、HR随推荐列表长度的变化;
图4为一种基于长短期兴趣的序列化推荐方法表示不同用户对长期偏好以及即时兴趣的依赖程度。
下面结合附图来进一步描述本发明的技术方案:
如图1所示,本发明处理数据集中的用户购买序列数据以及用户提问数据,据此得到用户与商品的序列化交互数据,提取用户对于商品的评论内容来表示商品的特征;接下来使用递归神经网络(Recursive Neural Network,RNN)从用户的历史购买序列数据中学习用户稳定的长期偏好,同时使用提问数据来建模用户的即时兴趣;最后,对于稳定的长期偏好和动态的即时兴趣,本文使用注意力(Attention)机制来刻画不同用户对这两个特征的依赖程度。具体方法,包括以下步骤,见图2:
S1:获取数据,对数据进行预处理;
根据一般数据处理方式,本发明中的预处理包括:将每个用户的购买数据、评论数据以及提问数据按照时间顺序排序、过滤总购买数低的用户。为保证推荐准确率,本实施例过滤掉了总购买数低于5次的用户。
S2:对所有的评论文本、提问文本进行处理,本发明使用TF-IDF方法对评论文本、提问文本进行处理。
对每个商品的相关文本中选择得分最高的k个词作为提取特征,通过所有特征的集合A={a
1,a
2,...,a
k}来对商品进行描述,表示为I={i
1,i
2,...,i
n},其中i
j表示第j个商品的特征表示;
S3:构建用户购买序列的向量表示。
根据商品的特征表示矩阵I以及用户的历史购买序列得到每个用户购买序列的向量表示,表示为:
S4:对用户的长期兴趣偏好进行表示。
h
j=o
j tanh(c
j)
其中i
j、f
j和o
j分别对应GRU的输入门、遗忘门和输出门,b
j为当前时刻的购物篮的向量表示,c
j为GRU记忆单元的值,
为偏置项,h
j为第j步隐藏状态;
和
分别表示使用双向RNN得到的隐藏单元值,将其拼接得到
因此用户u的长期偏好表示为:
longP
u=average(h
1,h
2,...,h
tq);
S5:对用户的短期兴趣偏好进行表示。
短期兴趣偏好建模主要使用CoreNLP算法对用户在t
q时刻的提问文本进行处理,得到用户在提问中比较关注的特征的分数,因此用户u的短期偏好形式上可以描述为:
S6:由S4得到的长期兴趣偏好和S5得到的短期兴趣偏好,通过Attention机制可以得到长短期兴趣相结合的用户聚合偏好。
所述聚合偏好表示为:
步骤7:使用交叉熵损失函数来学习模型的参数,得到提问时刻t
q后每个物品被购买的概率,描述为:
其中,γ表示历史购买序列中观察到的项,γ
-表示消极实例,可以将未观察的商品全体视为消极实例,也可以采取负采样的方式。
推荐结果如图3、图4所示,对用户下一个时刻可能购买的商品进行预测,得到预测评分向量,从而对用户推荐top-K个商品。
Claims (7)
- 一种基于长短期兴趣的序列化推荐方法,其特征在于:所述方法包括以下步骤:S1:获取数据,对数据进行预处理;S2:对所有的评论文本、提问文本进行处理,对每个商品的相关文本中选择得分最高的多个词作为提取特征,通过所有特征的集合来对商品进行描述,构建商品的特征表示矩阵;S3:构建用户购买序列的向量表示:根据商品的特征表示矩阵以及用户的历史购买序列得到每个用户购买序列的向量表示;S4:分别对用户的长期兴趣偏好和短期兴趣偏好进行表示;S5:将用户的长期兴趣偏好和短期兴趣偏好通过Attention机制获得用户聚合偏好;S6:通过确定聚合偏好和目标物品之间的关系,获得用户提问之后与物品交互的概率;S7:使用交叉熵损失函数来学习模型的参数,得到提问时刻后每个物品被购买的概率。
- 根据权利要求1所述的一种基于长短期兴趣的序列化推荐方法,其特征在于:所述S1中预处理包括:将每个用户的购买数据、评论数据以及提问数据按照时间顺序排序、过滤总购买数低的用户。
- 根据权利要求1所述的一种基于长短期兴趣的序列化推荐方法,其特征在于:所述S2中选择得分最高的多个词的个数为大于等于5个。
- 根据权利要求1所述的一种基于长短期兴趣的序列化推荐方法,其特征在于:所述S2中,使用TF-IDF方法对评论文本、提问文本进行处理。
- 根据权利要求1所述的一种基于长短期兴趣的序列化推荐方法,其特征在于:根据用户购买序列的向量表示,使用双向RNN隐藏单元的值对用户的长期偏好进行表示。
- 根据权利要求1所述的一种基于长短期兴趣的序列化推荐方法,其特征在于:短期兴趣偏好使用CoreNLP算法对用户在某一时刻的提问文本进行处理,得到用户在提问中比较关注的特征的分数,对用户的短期兴趣偏好进行表示。
- 根据权利要求1所述的一种基于长短期兴趣的序列化推荐方法,其特征在于:所述S6中的所述聚合偏好和目标物品之间的关系通过使用全连接层来确定。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US17/727,727 US20220301024A1 (en) | 2020-01-07 | 2022-04-23 | Sequential recommendation method based on long-term and short-term interests |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010014762.2A CN111242729A (zh) | 2020-01-07 | 2020-01-07 | 一种基于长短期兴趣的序列化推荐方法 |
CN202010014762.2 | 2020-01-07 |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US17/727,727 Continuation US20220301024A1 (en) | 2020-01-07 | 2022-04-23 | Sequential recommendation method based on long-term and short-term interests |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2021139164A1 true WO2021139164A1 (zh) | 2021-07-15 |
Family
ID=70879897
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2020/110549 WO2021139164A1 (zh) | 2020-01-07 | 2020-08-21 | 一种基于长短期兴趣的序列化推荐方法 |
Country Status (3)
Country | Link |
---|---|
US (1) | US20220301024A1 (zh) |
CN (1) | CN111242729A (zh) |
WO (1) | WO2021139164A1 (zh) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113722599A (zh) * | 2021-09-06 | 2021-11-30 | 中国计量大学 | 一种基于用户长期兴趣和短期兴趣建模的会话推荐方法 |
CN114741590A (zh) * | 2022-03-28 | 2022-07-12 | 西北大学 | 基于自注意力路由与Transformer的多兴趣推荐方法 |
CN115438259A (zh) * | 2022-09-05 | 2022-12-06 | 淮阴工学院 | 基于动态兴趣和知识图谱的冷链物流车源推荐方法及装置 |
Families Citing this family (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111242729A (zh) * | 2020-01-07 | 2020-06-05 | 西北工业大学 | 一种基于长短期兴趣的序列化推荐方法 |
CN111915395A (zh) * | 2020-07-07 | 2020-11-10 | 云境商务智能研究院南京有限公司 | 一种基于多视图注意力机制的旅游包推荐方法 |
CN112182387B (zh) * | 2020-09-29 | 2023-08-25 | 中国人民大学 | 一种时间信息增强的个性化搜索方法 |
CN112819575B (zh) * | 2021-01-26 | 2022-02-18 | 中国计量大学 | 一种考虑重复购买行为的会话推荐方法 |
CN112801745A (zh) * | 2021-02-02 | 2021-05-14 | 李海涛 | 基于大数据平台的在线评论有效性的推荐方法 |
CN112862007B (zh) * | 2021-03-29 | 2022-12-13 | 山东大学 | 基于用户兴趣编辑的商品序列推荐方法及*** |
CN113077313B (zh) * | 2021-04-13 | 2022-09-13 | 合肥工业大学 | 融合用户生成场景图像与个性化偏好的互补品推荐方法 |
CN113313381B (zh) * | 2021-05-28 | 2022-04-08 | 北京航空航天大学 | 一种用户交互敏感的动态图序列推荐*** |
CN114154071B (zh) * | 2021-12-09 | 2023-05-09 | 电子科技大学 | 一种基于注意力机制的情感时序推荐方法 |
CN114254205B (zh) * | 2021-12-30 | 2023-08-04 | 广东工业大学 | 一种基于音乐多模态数据的用户长短期偏好推荐预测方法 |
CN116127199B (zh) * | 2023-04-17 | 2023-06-16 | 昆明理工大学 | 一种针对服装序列推荐的用户偏好建模方法 |
CN117455629A (zh) * | 2023-11-24 | 2024-01-26 | 美服数字科技(广州)有限公司 | 一种直播带货智能推送方法及*** |
CN117745349A (zh) * | 2023-12-22 | 2024-03-22 | 广州市禾赢文化传播有限公司 | 一种基于用户特征的个性化推券方法及*** |
CN118014622B (zh) * | 2024-03-05 | 2024-07-12 | 武汉卓尔数字传媒科技有限公司 | 一种基于用户画像的广告推送方法及*** |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110097433A (zh) * | 2019-05-14 | 2019-08-06 | 苏州大学 | 基于注意力机制的推荐方法、装置、设备及存储介质 |
CN110245299A (zh) * | 2019-06-19 | 2019-09-17 | 中国人民解放军国防科技大学 | 一种基于动态交互注意力机制的序列推荐方法及其*** |
CN110334759A (zh) * | 2019-06-28 | 2019-10-15 | 武汉大学 | 一种评论驱动的深度序列推荐方法 |
CN110377840A (zh) * | 2019-07-29 | 2019-10-25 | 电子科技大学 | 一种基于用户长短期偏好的音乐列表推荐方法及*** |
CN111242729A (zh) * | 2020-01-07 | 2020-06-05 | 西北工业大学 | 一种基于长短期兴趣的序列化推荐方法 |
Family Cites Families (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP4751242B2 (ja) * | 2006-05-29 | 2011-08-17 | 日本電信電話株式会社 | リコメンド装置、リコメンド方法、リコメンドプログラムおよびそのプログラムを記録した記録媒体 |
WO2016191959A1 (zh) * | 2015-05-29 | 2016-12-08 | 深圳市汇游智慧旅游网络有限公司 | 一种时变的协同过滤推荐方法 |
US20200043026A1 (en) * | 2017-02-08 | 2020-02-06 | Whitehawk Cec Inc. | Decision support system and methods associated with same |
CN110490683B (zh) * | 2018-05-15 | 2022-04-12 | ***通信集团浙江有限公司 | 一种线下线上协同多模型混合推荐的方法及*** |
US20200311585A1 (en) * | 2019-03-31 | 2020-10-01 | Palo Alto Networks | Multi-model based account/product sequence recommender |
CN110008409A (zh) * | 2019-04-12 | 2019-07-12 | 苏州市职业大学 | 基于自注意力机制的序列推荐方法、装置及设备 |
TWM586402U (zh) * | 2019-07-24 | 2019-11-11 | 第一商業銀行股份有限公司 | 商品推薦系統 |
US11429982B2 (en) * | 2019-12-31 | 2022-08-30 | Paypal, Inc. | Identifying changes in user characteristics using natural language processing |
CN112905886B (zh) * | 2021-02-22 | 2022-02-08 | 中国计量大学 | 一种基于多兴趣重复网络的会话推荐方法 |
-
2020
- 2020-01-07 CN CN202010014762.2A patent/CN111242729A/zh active Pending
- 2020-08-21 WO PCT/CN2020/110549 patent/WO2021139164A1/zh active Application Filing
-
2022
- 2022-04-23 US US17/727,727 patent/US20220301024A1/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110097433A (zh) * | 2019-05-14 | 2019-08-06 | 苏州大学 | 基于注意力机制的推荐方法、装置、设备及存储介质 |
CN110245299A (zh) * | 2019-06-19 | 2019-09-17 | 中国人民解放军国防科技大学 | 一种基于动态交互注意力机制的序列推荐方法及其*** |
CN110334759A (zh) * | 2019-06-28 | 2019-10-15 | 武汉大学 | 一种评论驱动的深度序列推荐方法 |
CN110377840A (zh) * | 2019-07-29 | 2019-10-25 | 电子科技大学 | 一种基于用户长短期偏好的音乐列表推荐方法及*** |
CN111242729A (zh) * | 2020-01-07 | 2020-06-05 | 西北工业大学 | 一种基于长短期兴趣的序列化推荐方法 |
Non-Patent Citations (2)
Title |
---|
TAN QIAOQIAO, LIU FANG’AI: "Recommendation Based on Users’ Long-Term and Short-Term Interests with Attention", MATHEMATICAL PROBLEMS IN ENGINEERING, GORDON AND BREACH PUBLISHERS , BASEL, CH, vol. 2019, 17 October 2019 (2019-10-17), CH, pages 1 - 13, XP055826613, ISSN: 1024-123X, DOI: 10.1155/2019/7586589 * |
ZHANG YAN, GUO BIN;WANG QIAN-RU;ZHANG JING;YU ZHI-WEN: "SeqRec: Sequential-Based Recommendation Model with Long-Term Preference and Instant Interest", JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), GAIKAN BIANJIBU, HANGZHOU, CN, vol. 54, no. 6, 1 June 2020 (2020-06-01), CN, pages 1177 - 1184, XP055826644, ISSN: 1008-973X, DOI: 10.3785/j.issn.1008-973X.2020.06.015 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113722599A (zh) * | 2021-09-06 | 2021-11-30 | 中国计量大学 | 一种基于用户长期兴趣和短期兴趣建模的会话推荐方法 |
CN114741590A (zh) * | 2022-03-28 | 2022-07-12 | 西北大学 | 基于自注意力路由与Transformer的多兴趣推荐方法 |
CN115438259A (zh) * | 2022-09-05 | 2022-12-06 | 淮阴工学院 | 基于动态兴趣和知识图谱的冷链物流车源推荐方法及装置 |
Also Published As
Publication number | Publication date |
---|---|
US20220301024A1 (en) | 2022-09-22 |
CN111242729A (zh) | 2020-06-05 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2021139164A1 (zh) | 一种基于长短期兴趣的序列化推荐方法 | |
CN108648049B (zh) | 一种基于用户行为区别建模的序列推荐方法 | |
CN108629665B (zh) | 一种个性化商品推荐方法和*** | |
Pan et al. | Study on convolutional neural network and its application in data mining and sales forecasting for E-commerce | |
CN110956497B (zh) | 一种电子商务平台用户重复购买行为预测方法 | |
CN111222332B (zh) | 一种结合注意力网络和用户情感的商品推荐方法 | |
Bali et al. | R: Unleash machine learning techniques | |
CN111709810A (zh) | 基于推荐模型的对象推荐方法及装置 | |
CN109584006B (zh) | 一种基于深度匹配模型的跨平台商品匹配方法 | |
CN113190751B (zh) | 一种融合关键词生成的推荐方法 | |
Shen et al. | A voice of the customer real-time strategy: An integrated quality function deployment approach | |
CN110851694A (zh) | 基于用户记忆网络和树形结构的深度模型的个性化推荐*** | |
Wang et al. | Enhancing Long Tail Recommendation Based on User's Experience Evolution | |
Yu et al. | A graph attention network under probabilistic linguistic environment based on Bi-LSTM applied to film classification | |
Wang et al. | Jointly modeling intra-and inter-transaction dependencies with hierarchical attentive transaction embeddings for next-item recommendation | |
Lu | Knowledge distillation-enhanced multitask framework for recommendation | |
Agarwal et al. | Binarized spiking neural networks optimized with Nomadic People Optimization-based sentiment analysis for social product recommendation | |
Jalilifard et al. | Friendship is all we need: A multi-graph embedding approach for modeling customer behavior | |
Jabir et al. | Big data analytics for strategic and operational decisions | |
Shedekar et al. | Enhancing E-Commerce Insights: Sentiment Analysis Using Machine Learning and Ensemble Techniques | |
Chen et al. | Application of SVM model based on collaborative filtering hybrid algorithm in e-commerce recommendation | |
CN112182165B (zh) | 基于在线评论的新产品质量规划方法 | |
Sheng et al. | Global and session item graph neural network for session-based recommendation | |
Zhang et al. | MGCN4REC: Multi-graph Convolutional Network for Next Basket Recommendation with Instant Interest | |
Nedjah et al. | Client profile prediction using convolutional neural networks for efficient recommendation systems in the context of smart factories |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 20913006 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 20913006 Country of ref document: EP Kind code of ref document: A1 |