AI 中文总结
针对员工职业抱负对个人与组织成长的重要性，提出结合职业路径相似度（CPS）与抱负相似度（AS）的数据驱动方法推荐个性化职业路径，定义了领域知识驱动（DKD）和无监督表示学习与对齐（URLA）两种CPS计算方法，URLA方法无需领域知识且包含时间维度，在DCG指标上优于DKD。
AI 中文摘要
实现职业抱负对员工和组织的成长都很重要。我们提出一种数据驱动方法，为给定求职者的职业路径和抱负推荐个性化职业路径。该方法利用求职者职业与候选职业路径之间的职业路径相似度（CPS），以及抱负与候选职业路径之间的“抱负相似度”（AS）来寻找合适的职业路径。CPS确保个性化推荐，而AS确保实现抱负。我们定义了两种计算职业路径之间CPS的方法：（a）领域知识驱动（DKD），（b）基于无监督表示学习与对齐（URLA），以及不同的AS度量方法。基于DKD的相似度根据从职业路径中提取和汇总的特征来定义。在URLA中，我们利用员工职业路径中存在的事件名称序列来学习每个事件名称的嵌入。在URLA中，我们使用学习到的职业路径事件名称的嵌入向量以及相关的事件属性（技能簇和领域）来找到两条职业路径之间的最佳对齐。我们假设事件名称在序列中的相对位置代表事件名称的语义，且该语义可以被学习。我们使用LSTM神经网络来学习每个职业事件名称的嵌入向量。我们还定义了在两种提出的方法中计算抱负与职业路径之间AS的匹配方法。我们结合CPS和AS对员工可用的“候选职业路径”进行排名，以找到合适的路径。与DKD相比，URLA方法获得了更好的DCG值。我们还表明，两种方法的排名是一致的。URLA方法更好，因为它不需要领域知识来建模相似度，并且通过使用加权余弦距离的最优Levenshtein对齐包含了时间维度。
英文摘要
Fulfilling career aspirations is important for growth of employee and organization. We propose a data driven methodology to recommend personalized career path for a given aspirant's career path and aspirations. The pro-posed method uses the career path similarity (CPS) between aspirant's career and candidate career path, and 'aspirational similarity' (AS) between aspiration and candidate career paths to find suitable career path. CPS ensures personalized recommendation while AS ensures aspiration fulfillment. We defined two methods to compute the CPS between career paths which are (a) domain knowledge driven (DKD) and, (b) unsupervised representation learning and alignment (URLA) based, along with different AS measures. The DKD based similarity is defined in the terms of features extracted and summarized over career paths. In the URLA, we use the sequence of event names present in the career paths of the employees to learn the embedding for each event name. In URLA we use learned embedding vector of the career path event names and as-sociated event attributes (skill cluster and domain) to find the best alignment between two career paths. We hypothesized that relative position of event names in the sequence represents semantics of event name and that can be learned. We use LSTM neural network to learn the embedding vector of each career event name. We also define the matching method to compute the AS be-tween aspiration and career path in both proposed methods. We combine CPS and AS to rank available 'candidate career paths' of employees to find the suitable one. We get better DCG value in URLA as compare to DKD. We also showed that ranking are coherent using both the methods. URLA method is better since it does not require domain knowledge to model the similarity and includes temporal aspect by optimal Levenshtein alignment using weighted cosine distance.
